WO2023085120A1 - Information processing method, information processing device, program, and information processing system - Google Patents

Information processing method, information processing device, program, and information processing system Download PDF

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Publication number
WO2023085120A1
WO2023085120A1 PCT/JP2022/040344 JP2022040344W WO2023085120A1 WO 2023085120 A1 WO2023085120 A1 WO 2023085120A1 JP 2022040344 W JP2022040344 W JP 2022040344W WO 2023085120 A1 WO2023085120 A1 WO 2023085120A1
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Prior art keywords
user
information processing
information
daily necessities
held
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PCT/JP2022/040344
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French (fr)
Japanese (ja)
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厚志 大久保
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ソニーグループ株式会社
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Publication of WO2023085120A1 publication Critical patent/WO2023085120A1/en

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • the present disclosure relates to an information processing method, an information processing device, a program, and an information processing system.
  • a measurement method for objectively measuring a patient's (user's) muscle function has been established.
  • the patient's dominant hand is made to hold an object with a predetermined weight and make a predetermined movement, and the patient's muscle function is evaluated based on the state of that movement. .
  • an information processing method capable of measuring a user's behavior in daily life and scoring the user's state without imposing a burden on the user. Propose a treatment system.
  • the information processing device acquires first sensing data generated from movement of the daily necessities by the user's actions from a first sensor provided on the daily necessities held by the user;
  • An information processing method is provided, including estimating a judgment index related to the user's disease based on first sensing data and weight information related to the daily item when held by the user.
  • an acquisition unit that acquires first sensing data generated from movement of the daily necessities by the user's action from a first sensor provided on the daily necessities held by the user; and an estimating unit for estimating a judgment index related to the user's disease, based on sensing data of and weight information related to the daily necessities when held by the user.
  • an acquisition unit configured to acquire first sensing data generated from movement of the daily necessities by the user's actions from a first sensor provided on the daily necessities held by the user
  • a program functions as an estimating unit that estimates a judgment index regarding the user's disease based on the first sensing data and weight information regarding the daily item when the user holds it.
  • a first sensor that is provided in a daily item held by a user and that acquires first sensing data resulting from movement of the daily item by the user's action; the first sensing data; and an information processing device for estimating a judgment index related to the user's disease based on weight information related to the daily item when held by the user.
  • FIG. 11 is a table for explaining an example of muscular dystrophy examination
  • FIG. 1 is an explanatory diagram (1) for explaining an example of a daily necessities 100 used in an embodiment of the present disclosure
  • FIG. 2 is an explanatory diagram (Part 2) for explaining an example of the daily necessities 100 used in the embodiment of the present disclosure
  • FIG. 3 is an explanatory diagram (part 3) for explaining an example of the daily necessities 100 used in the embodiment of the present disclosure
  • 1 is a system diagram showing a schematic functional configuration of an information processing system 10 according to a first embodiment of the present disclosure
  • FIG. 2 is a block diagram showing a functional configuration example of a sensor unit 200 according to the first embodiment of the present disclosure
  • FIG. 2 is a block diagram showing an example functional configuration of a user terminal 300 according to the first embodiment of the present disclosure
  • FIG. 2 is a block diagram showing a functional configuration example of a server 400 according to the first embodiment of the present disclosure
  • FIG. 2 is a block diagram showing a functional configuration example of a terminal for medical staff 500 according to the first embodiment of the present disclosure
  • FIG. 1 is a sequence diagram of an information processing method according to the first embodiment of the present disclosure
  • FIG. 1 is a flowchart (part 1) of an information processing method according to the first embodiment of the present disclosure
  • FIG. 4 is a diagram showing an example of an inspection item table 482 according to the first embodiment of the present disclosure
  • FIG. 2 is a flowchart (part 2) of an information processing method according to the first embodiment of the present disclosure
  • 4 is a diagram showing an example of a determination table 484 according to the first embodiment of the present disclosure
  • FIG. 6 is a flow chart of an information processing method according to a second embodiment of the present disclosure
  • 8 is a flowchart of an information processing method according to a third embodiment of the present disclosure
  • FIG. 11 is an explanatory diagram for describing a third embodiment of the present disclosure
  • FIG. FIG. 10 is a system diagram showing a schematic functional configuration of an information processing system 10a according to a modified example of the embodiment of the present disclosure
  • FIG. 10 is a system diagram showing a schematic functional configuration of an information processing system 10b according to a modified example of the embodiment of the present disclosure
  • FIG. 11 is a block diagram showing a functional configuration example of a server 600 according to a modified example of the embodiment of the present disclosure
  • FIG. FIG. 11 is a diagram showing an example of a table 490 according to a modified example of the embodiment of the present disclosure
  • FIG. 3 is a block diagram showing an example of hardware configuration
  • FIG. 1A is an explanatory diagram for explaining an example of testing for muscular dystrophy
  • FIG. 1B is a table for explaining an example of testing for muscular dystrophy.
  • Muscular dystrophy is a general term for hereditary muscle diseases whose main lesion is necrosis/regeneration of skeletal muscle, and all of these diseases are caused by mutations in genes related to the production of proteins essential for muscles.
  • protein function is impaired due to gene mutation, normal cell function cannot be maintained, resulting in muscle deformation and necrosis, resulting in decreased muscle strength and motor dysfunction. symptoms.
  • the main symptoms of muscular dystrophy are decreased motor function due to interosseous muscle damage, but contractures (joints become stiff and range of motion narrows), osteoporosis, respiratory dysfunction, myocardial dysfunction, and swallowing. It may be accompanied by various functional disorders and complications such as functional disorders, bone metabolism disorders, and central nervous system disorders.
  • a measurement method that objectively measures the muscle function of patients is important.
  • a method such as the Performance of Upper Limb (PUL) disability degree classification (9-step method) is used.
  • PUL Performance of Upper Limb
  • FIGS. 1A and 1B the dominant hand of the patient is allowed to hold an object having a predetermined weight and perform a predetermined motion, or the patient's dominant hand is allowed to perform a predetermined motion without holding the object. Then, the patient's muscle function is evaluated based on the state of the movement.
  • FIGS. 2A to 2C are explanatory diagrams for explaining an example of daily necessities 100 used in the embodiment of the present disclosure.
  • IMUs Inertial Measurement Units
  • daily necessities include tableware (glasses, cups, bowls, plates, forks, spoons, knives, etc.), cooking utensils (knives, etc.), toothbrushes, hairbrushes, dryers, towels, shoehorns, clothes, hats, bags, writing utensils
  • Examples include tools, mobile terminals, and furniture (chairs, etc.).
  • the IMU mounted on daily necessities is a sensor that detects three-axis acceleration, three-axis angular velocity, etc. that change due to the movement of daily necessities due to the movement of the patient, based on the sensing data output from the IMU, can be estimated. Furthermore, the motion of the patient's hand holding the daily necessities can be measured from the estimated movement trajectory of the daily necessities.
  • FIG. 2A shows a cup as the daily article 100a, and the X, Y, and Z axes are defined with a predetermined point on the cup as a reference point, and the IMU measures the acceleration and angular velocity of these three axes. can do.
  • FIG. 2B illustrates a cup as daily necessities 100b
  • FIG. 2C illustrates a spoon as daily necessities 100c.
  • the patient can use it in their daily life without burdening the patient. can be measured. Then, in the embodiment of the present disclosure, the patient's motion is measured, the patient's disease state is scored based on the measurement results, and the user's disease state is evaluated according to the score. Furthermore, in the embodiment of the present disclosure, if it is determined to be necessary based on the evaluation results, an alert or the like is sent to the medical staff, the user, and the user's family to encourage the user to go to the hospital for a detailed examination. Send automatically. As a result, according to this embodiment, it is possible to provide patients with an opportunity for early detection and early treatment.
  • the patient in the evaluation of muscle function, is required to hold an object of a predetermined weight with the dominant hand and perform a predetermined action.
  • the patient may be required to grasp objects of various weights.
  • the daily necessities 100 such as cups and cups shown in FIGS. 2A and 2B can change the weight of the entire daily necessities 100 according to the amount of liquid (object) contained therein. Therefore, in the embodiment of the present disclosure, by providing an IMU to such daily necessities 100 whose overall weight can change, it is possible to measure the patient's movements when gripping articles of various weights. . Specifically, in the embodiment of the present disclosure, by providing a weight sensor or the like in daily necessities 100 whose overall weight can change, the weight of objects included in the daily necessities is measured.
  • Examples of daily necessities 100 whose overall weight can change include glasses, cups, plates, water bottles, pots, kettles, and bags.
  • an embodiment of the present disclosure may use a household item 100 whose overall weight does not change, such as the spoon shown in FIG. 2C.
  • daily necessities 100 with various weights that exist around the patient in daily life are used to measure the patient's movements when gripping articles of various weights. be able to. The details of such embodiments of the present disclosure will be sequentially described below.
  • FIG. 3 is a system diagram showing a schematic functional configuration of the information processing system 10 according to the first embodiment of the present disclosure.
  • the information processing system 10 includes a sensor unit 200 provided in a daily necessities 100, a user terminal 300, a server (information processing device) 400, and a medical staff terminal 500. , which are communicably connected to each other via a network 800 .
  • the user terminal 300, the server 400, and the medical staff terminal 500 are connected to the network 800 via a base station (for example, a mobile phone base station, a wireless LAN (Local Area network) access point, etc.) (not shown).
  • the communication method used in the network 800 can be any method regardless of whether it is wired or wireless (for example, WiFi (registered trademark), Bluetooth (registered trademark), etc.), but stable operation is maintained. It is desirable to use a communication method that can Below, an outline of each device included in the information processing system 10 according to the present embodiment will be described.
  • the sensor unit 200 is installed in the daily necessities 100 used by the user on a daily basis.
  • the sensor unit 200 includes at least an inertial measurement unit (IMU) (first sensor), and can measure movement of the daily article 100 that is gripped by the user and moved by the user's action.
  • the sensor unit 200 includes a weight sensor (second sensor) and the like for measuring the weight of the liquid in the cup and the solid on the plate when the daily article 100 is a cup or a plate. good too.
  • the sensor unit 200 can transmit sensing data and the like to the user terminal 300, which will be described later, via short-range wireless communication (for example, WiFi (registered trademark), Bluetooth (registered trademark), etc.). Details of the daily necessities 100 and the sensor unit 200 will be described later.
  • the user terminal 300 is a terminal that the user uses on a daily basis, and is capable of transmitting sensing data from the sensor unit 200 to the server 400 and receiving notifications from the server 400 .
  • the user terminal 300 can be a device such as a tablet, a smart phone, a mobile phone, a laptop PC (Personal Computer), a desktop PC, or a Head Mounted Display (HMD).
  • a tablet such as a tablet, a smart phone, a mobile phone, a laptop PC (Personal Computer), a desktop PC, or a Head Mounted Display (HMD).
  • HMD Head Mounted Display
  • the user terminal 300 when the user terminal 300 is a tablet, smartphone, mobile phone, or the like, the user terminal 300 itself can function as the daily necessities 100 provided with the sensor unit 200. Furthermore, in the present embodiment, the sensor unit 200 may perform a part of the functions of the user terminal 300 (for example, transmission/reception and output of information). may not be included. Details of the user terminal 300 will be described later.
  • the server 400 provides a score for the user's disease based on sensing data (first sensing data) acquired from the sensor unit 200 via the user terminal 300 and information on the weight of the daily necessities 100 when held by the user. (determination index) can be estimated.
  • the server 400 is configured by, for example, a computer. A detailed configuration of the server 400 will be described later.
  • Medical staff terminal 500 is a terminal used by a medical staff such as a doctor, and is capable of receiving notifications from server 400 via network 800 .
  • the medical worker terminal 500 can be a device such as a tablet, smart phone, mobile phone, laptop PC, desktop PC, or the like.
  • the terminal for medical personnel 500 may be a terminal used by the user's family. Details of the medical staff terminal 500 will be described later.
  • FIG. 3 shows the information processing system 10 according to this embodiment as including a pair of sensor units 200 and user terminals 300
  • the information processing system 10 according to the present embodiment may include pairs of multiple sensor units 200 and one user terminal 300 .
  • a plurality of sensor units 200 and one or a plurality of user terminals 300 may be paired.
  • the information processing system 10 according to the embodiment includes, for example, a plurality of terminals 500 for medical staff, other communication devices such as a relay device for transmitting sensing data from the sensor unit 200 to the user terminal 300, and the like. may contain.
  • the daily necessities 100 are articles that are held by a user's hand and used in daily life.
  • the daily necessities 100 include tableware (glasses, cups, bowls, plates, forks, spoons, knives, etc.), cooking utensils (kitchen knives, etc.), toothbrushes, hairbrushes, dryers, towels, shoehorns, Clothes, hats, bags, writing utensils, tools, mobile terminals (smartphones, tablets, mobile phones, etc.), and furniture (chairs, etc.) can be mentioned.
  • the daily necessities 100 are preferably articles whose overall weight can be changed according to the amount of objects contained therein.
  • articles include glasses and cups. , plates, water bottles, pots, kettles, and bags.
  • FIG. 4 is a block diagram showing a functional configuration example of the sensor unit 200 according to this embodiment.
  • the sensor unit 200 according to this embodiment mainly includes an IMU (first sensor) 210, a weight sensor (second sensor) 220, a control section 230, and a communication section 270. have.
  • IMU first sensor
  • second sensor weight sensor
  • control section 230 control section
  • communication section 270 communication section
  • the IMU 210 is a sensor that detects 3-axis acceleration, 3-axis angular velocity, and the like (first sensing data) that change due to movement of the daily item 100 by the action of the user holding the daily item 100 .
  • the IMU 210 includes an acceleration sensor that is an inertial sensor that acquires acceleration, a gyro sensor that is an inertial sensor that acquires angular velocity, and the like.
  • the IMU 210 may include a geomagnetic sensor, an atmospheric pressure sensor, etc. instead of or in addition to the inertial sensor.
  • the weight sensor 220 is provided, for example, on the inner bottom surface of the daily necessities 100 such as a cup, and can measure the weight of the liquid or solid contained in the daily necessities 100 . Further, the weight sensor 220 is not limited to being a sensor that directly measures the weight, but sensing data (second sensing data) capable of estimating the weight of the object included in the daily necessities 100 ) may be used. In this embodiment, instead of the weight sensor 220, for example, a pressure sensor, a resistance sensor (for example, measuring the resistance value of liquid), a vibration sensor (for example, measuring vibration of liquid), a photoreflector sensor ( For example, measuring the height of the liquid) may be used.
  • a pressure sensor for example, a resistance sensor (for example, measuring the resistance value of liquid), a vibration sensor (for example, measuring vibration of liquid), a photoreflector sensor ( For example, measuring the height of the liquid) may be used.
  • the weight sensor 220 may not be provided in the sensor unit 200 if the daily product 100 does not change in overall weight. Furthermore, in the present embodiment, if the weight of an object included in the daily necessities 100 can be estimated from sensing data such as triaxial acceleration and triaxial angular velocity obtained from the IMU 210, the sensor unit 200 can detect the weight. The sensor 220 may not be provided. In the latter case, for example, if the vibration of a glass immersed in liquid can be estimated from sensing data such as three-axis acceleration and three-axis angular velocity, the weight of the liquid in the glass can be estimated from the estimated vibration. may
  • the control unit 230 can control overall measurement in the sensor unit 200, such as controlling reading (sampling) timing of sensing data of the IMU 210 and the weight sensor 220 based on a predetermined synchronization signal or the like.
  • the control unit 230 may further have a storage unit (not shown), and the storage unit may store various parameters and the like for controlling the IMU 210, the weight sensor 220, and the like.
  • the control unit 230 incorporates a clock mechanism (not shown) for grasping the accurate time in order to link the sensing data from the IMU 210 and the weight sensor 220 with the time and output it to the user terminal 300 and the server 400.
  • control unit 230 is realized by, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. Some or all of the functions performed by the control unit 230 may be performed by the user terminal 300, which will be described later.
  • a CPU Central Processing Unit
  • ROM Read Only Memory
  • RAM Random Access Memory
  • the communication unit 270 can transmit and receive information (for example, sensing data) to and from an external device such as the user terminal 300 .
  • the communication unit 270 can be said to be a communication interface having a function of transmitting and receiving data.
  • the communication unit 270 is realized by communication devices such as a communication antenna, a transmission/reception circuit, and a port.
  • the sensor unit 200 may include, for example, other sensors provided around the user in the sensor unit 200 or separately from the sensor unit 200, in addition to the above-described IMU 210 and the like.
  • Other sensors include, for example, GPS (Global Positioning System) receivers, temperature sensors, humidity sensors, biological information sensors that acquire user's biological information (heartbeat sensor, blood flow sensor, blood pressure sensor, electroencephalogram sensor, body temperature sensor, etc. ) can be mentioned.
  • the sensor unit 200 may be configured as a device integrated with the user terminal 300 described later. Note that the sensor unit 200 according to the present embodiment is not limited to the configuration example shown in FIG. 4, and may further include other functional units, for example.
  • FIG. 5 is a block diagram showing a functional configuration example of the user terminal 300 according to this embodiment.
  • the user terminal 300 according to this embodiment mainly includes a control unit 330, an input unit 350, an output unit 360, a communication unit 370, and a storage unit 380, as shown in FIG.
  • Each functional unit of the user terminal 300 will be described below.
  • the controller 330 can control each block of the user terminal 300 .
  • the control unit 330 is implemented by hardware such as CPU, ROM, and RAM, for example.
  • the input unit 350 can accept input of data and commands to the user terminal 300 . More specifically, the input unit 350 is implemented by a touch panel, keyboard, microphone, or the like.
  • the output unit 360 includes, for example, a display, a speaker, a lamp, a video output terminal, an audio output terminal, etc., and can output various information to the user by means of images, blinking, audio, and the like.
  • the communication unit 370 can transmit and receive information to and from external devices such as the sensor unit 200 and the server 400 , and can transmit sensing data from the sensor unit 200 to the server 400 , for example.
  • the communication unit 370 is a communication interface having a function of transmitting and receiving data, and is specifically realized by a communication device such as a communication antenna, a transmission/reception circuit, and a port.
  • the storage unit 380 can store programs, information, etc. for the above-described control unit 330 to execute various processes, and information obtained by the processes.
  • the storage unit 380 is realized by, for example, a magnetic recording medium such as a hard disk (HD), a nonvolatile memory such as a flash memory, or the like.
  • the user terminal 300 is not limited to the configuration example shown in FIG. 5, and may further include other functional units, for example.
  • FIG. 6 is a block diagram showing a functional configuration example of the server 400 according to this embodiment.
  • the server 400 mainly includes a processing unit 430, an input unit 450, an output unit 460, a communication unit 470, and a storage unit 480, as shown in FIG.
  • Each functional unit of the server 400 will be described below.
  • the processing unit 430 can control each block of the server 400 .
  • the processing unit 430 is implemented by hardware such as a CPU, ROM, and RAM, for example.
  • the processing unit 430 can estimate a score (determination index) regarding the user's disease based on sensing data from the sensor unit 200 and weight information regarding the daily necessities 100 when held by the user.
  • the processing unit 430 can also evaluate the state of the user's disease based on the estimated judgment index regarding the user's disease, and generate and output an evaluation result.
  • the processing unit 430 functions as an acquisition unit 432, an estimation unit 434, an evaluation unit 436, and an information output unit (output unit) 438 in order to realize these functions described above. Details of these functions of the processing unit 430 according to the present embodiment will be described below.
  • the acquisition unit 432 can acquire sensing data and the like transmitted from the sensor unit 200 via the user terminal 300 and output the acquired sensing data and the like to the estimation unit 434 described later. Specifically, the acquisition unit 432 acquires, for example, sensing data from the IMU 210 and the weight sensor 220 of the sensor unit 200, and identification information associated with the daily necessities 100 and the user terminal 300, and identification information identifying the user. etc. can be obtained.
  • Estimation unit 434 applies a predetermined algorithm based on the sensing data transmitted from the sensor unit 200 and information on the weight of the daily necessities 100 when held by the user to obtain the user's disease score (judgment index) can be estimated. Furthermore, the estimation unit 434 can output the estimated score to the evaluation unit 436 and the storage unit 480, which will be described later.
  • the estimating unit 434 retrieves the daily necessities linked to the identification information from a database stored in advance in the storage unit 480. Obtain information on the weight of the 100 itself. Furthermore, the estimation unit 434 acquires sensing data from the weight sensor 220 from the sensor unit 200 and estimates the weight of the liquid or the like included in the daily necessities 100 . Based on the weight of the daily necessities 100 themselves and the estimated weight of the liquid or the like in the daily necessities 100, the estimating unit 434 calculates the sum of the weights of the daily necessities 100 held by the user (the sum of the weights of the daily necessities 100 held by the user). weight information).
  • the estimating unit 434 acquires information on the weight of the daily necessities 100 themselves from the database stored in the storage unit 480 so that the user can Information about the weight of the daily necessities 100 when held can be obtained.
  • the estimation unit 434 estimates the movement trajectory of the daily necessities 100 based on sensing data including acceleration data and angular velocity data. Specifically, for example, the estimation unit 434 integrates the acceleration and angular velocity in the X-axis, Y-axis, and Z-axis with a predetermined point on the daily necessities 100 as a reference point, thereby obtaining the reference point on the three-dimensional space. It is possible to estimate the movement trajectory and posture change of
  • the estimating unit 434 selects the weight of the daily necessities 100 when held by the user, estimated as described above, and the movement trajectory of the daily necessities 100 from among a plurality of pre-stored inspection items. , to extract inspection items that can be linked to the user's action when holding the daily necessities 100 .
  • the estimating unit 434 has a motion similar to the motion when the user grips the daily necessities 100 from the inspection item table 482 for inspecting the state of disease as shown in FIG. 10, for example. Extract inspection items.
  • the estimation unit 434 may refer to previously acquired user attribute information (for example, information such as sex, age, height, and weight), or sensing data from other sensors such as a biological information sensor. You may refer to Then, the estimation unit 434 estimates a score (determination index) related to the user's disease based on a score calculation formula (information related to the extracted inspection item) or the like linked to the extracted inspection item.
  • the estimating unit 434 is not limited to estimating the weight of the liquid or the like included in the daily necessities 100 based on sensing data from the weight sensor 220 .
  • the estimating unit 434 uses an algorithm created in advance by machine learning for sensing data including acceleration data and angular velocity data from the IMU 210 (for example, vibration of the daily necessities 100 obtained from these sensing data). By performing the analysis, the weight of the liquid or the like contained in the daily necessities 100 may be estimated.
  • the evaluation unit 436 evaluates the user's disease state based on the user's disease score (determination index) estimated by the estimation unit 434, generates an evaluation result, and outputs the information output unit 438 and storage unit described later. 480. Specifically, the evaluation unit 436 extracts, for example, from the determination table 484 shown in FIG. 12, evaluation conditions linked to the inspection items extracted when estimating the score. Furthermore, the evaluation unit 436 evaluates whether the user's condition is normal or abnormal according to the extracted evaluation conditions, and if it is determined to be abnormal, an alarm is sent to the medical staff terminal 500, which will be described later. An instruction is output to the information output unit 438 .
  • the evaluation method in the evaluation unit 436 is not limited to the method described above, and other methods may be used in this embodiment.
  • the score for the same test item may be recorded, and whether the user's condition is normal or abnormal may be evaluated based on the change in the score over time.
  • the evaluation unit 436 evaluates as abnormal when the score changes by a predetermined threshold value or more within a predetermined period.
  • the information output unit 438 can control the communication unit 470 to be described later to transmit the evaluation results and the like from the evaluation unit 436 to the medical staff terminal 500 and the user terminal 300 .
  • the input unit 450 can accept input of data and commands to the server 400 . More specifically, the input unit 450 is implemented by a touch panel, keyboard, or the like.
  • the output unit 460 is configured by, for example, a display or the like, and can output various kinds of information in the form of images or the like.
  • the communication unit 470 can transmit and receive information (for example, alarms) to and from external devices such as the user terminal 300 and the terminal 500 for medical staff.
  • the communication unit 470 can be said to be a communication interface having a function of transmitting and receiving data, and is specifically realized by a communication device such as a communication antenna, a transmission/reception circuit, and a port.
  • the storage unit 480 can store programs, information, and the like for the processing unit 430 to execute various types of processing, and information obtained by the processing. Specifically, the storage unit 480 stores the user's disease state based on information on a plurality of test items for testing the disease state (for example, a test item table 482) and an estimated score on the user's disease. Stores information (for example, the determination table 484) used when evaluating the . In addition, the storage unit 480 may store user attribute information, information on the user's family, information on the weight of the daily necessities 100 used by the user, an estimated score (determination index), etc., as user data 486. good.
  • the storage unit 480 may store an estimation model 488 or the like, which is a model or algorithm used for estimation by the estimation unit 434 described above.
  • the storage unit 480 is implemented by, for example, a magnetic recording medium such as a hard disk, or a non-volatile memory such as a flash memory.
  • the server 400 is not limited to the configuration example shown in FIG. 6, and may further include other functional units, for example. Furthermore, the server 400 may be composed of a plurality of information processing devices communicably connected to each other via the network 800 . At least part of the functions of the server 400 may be executed by the user terminal 300 described above, or the server 400 may be configured as a device integrated with the user terminal 300 or the terminal 500 for medical staff. good too.
  • FIG. 7 is a block diagram showing a functional configuration example of the medical staff terminal 500 according to this embodiment.
  • the medical staff terminal 500 according to this embodiment mainly includes a control unit 530, an input unit 550, an output unit 560, a communication unit 570, and a storage unit 580, as shown in FIG.
  • Each functional unit of the medical staff terminal 500 will be described below.
  • control unit 530 can control each block of the medical staff terminal 500 .
  • the control unit 530 is implemented by hardware such as CPU, ROM, and RAM, for example.
  • the input unit 550 can accept input of data and commands to the medical staff terminal 500 . More specifically, the input unit 550 is implemented by a touch panel, keyboard, microphone, or the like.
  • the output unit 560 is composed of, for example, a display, a speaker, a lamp, a video output terminal, an audio output terminal, etc., and can output various information to the medical staff by means of images, flashes, sounds, and the like.
  • the communication unit 570 can transmit and receive information (for example, alarms) to and from an external device such as the server 400 .
  • the communication unit 570 can be said to be a communication interface having a function of transmitting and receiving data, and is specifically realized by a communication device such as a communication antenna, a transmission/reception circuit, and a port.
  • the storage unit 580 can store programs, information, etc. for the above-described control unit 530 to execute various processes, and information obtained by the processes.
  • the storage unit 580 is realized by, for example, a magnetic recording medium such as a hard disk, a non-volatile memory such as a flash memory, or the like.
  • medical staff terminal 500 is not limited to the configuration example shown in FIG. 7, and may further include other functional units, for example.
  • FIG. 8 is a sequence diagram of the information processing method according to this embodiment. As shown in FIG. 8, the information processing method according to this embodiment includes a plurality of steps from step S100 to step S500. Details of each step included in the information processing method according to the present embodiment will be described below.
  • the sensor unit 200 transmits, to the user terminal 300, the sensing data from the IMU 210 and the weight sensor 220 as well as the identification information identifying the daily necessities 100 on which it is mounted and the identification information identifying itself (step S100).
  • the user terminal 300 transmits identification information for identifying itself or identification information for identifying the user to the server 400 together with the sensing data and identification information acquired from the sensor unit 200 in step S100 described above (step S200).
  • the server 400 estimates the score regarding the user's disease based on the sensing data and identification information acquired from the user terminal 300 in step S200 described above (step S300). Details of the step S300 will be described later.
  • the server 400 evaluates the user's disease state based on the score estimated in step S300 described above, and transmits an alarm to the medical staff terminal 500 or the user terminal 300 according to the evaluation result (step S400). . Details of the step S400 will be described later.
  • the medical staff terminal 500 and the user terminal 300 output an alarm to the medical staff, the user, and the family based on the received alarm (step S500).
  • the content of the alarm may include information prompting the user to perform a detailed examination or information prompting treatment.
  • FIG. 9 is a flowchart of the information processing method according to this embodiment
  • FIG. 10 is a diagram showing an example of the inspection item table 482 according to this embodiment.
  • the estimation method according to this embodiment includes a plurality of steps from step S301 to step S305. Details of each step included in the estimation method according to the present embodiment will be described below.
  • the server 400 estimates the weight of the daily necessities 100 held by the user (step S301). For example, based on the identification information linked to the daily necessities 100 transmitted together with the sensing data from the sensor unit 200, the server 400 retrieves the daily necessities 100 linked to the identification information from a database stored in advance in the storage unit 480. Get weight information. Furthermore, the server 400 acquires sensing data from the weight sensor 220 from the sensor unit 200 and estimates the weight of the liquid or the like included in the daily necessities 100 . Based on the weight of the daily necessities 100 themselves and the estimated weight of the liquid or the like in the daily necessities 100, the estimating unit 434 calculates the sum of the weights of the daily necessities 100 held by the user (the sum of the weights of the daily necessities 100 held by the user). weight information).
  • server 400 detects that the user has held daily necessities 100 based on the change in sensing data from IMU 210 (step S302).
  • the server 400 estimates the movement trajectory of the daily necessities 100 moved by the user's action based on sensing data including acceleration data, angular velocity data, etc. (step S303). For example, the server 400 integrates the acceleration and angular velocity on the X-axis, Y-axis, and Z-axis with a predetermined point of the daily article 100 as a reference point, and calculates the movement trajectory and attitude change of the reference point in the three-dimensional space. can be estimated.
  • server 400 selects daily necessities 100 from inspection item table 482 as shown in FIG. is extracted (step S304).
  • the server 400 for example, extracts inspection items having weight information close to the weight of the daily necessities 100 held by the user and track information similar to the moving track of the daily necessities 100 .
  • the inspection item table 482 stores, for example, trajectory information, weight information, and the like associated with each measurement item as shown in FIGS. 1A and 1B.
  • the server 400 estimates the score (determination index) related to the user's disease based on the calculation formula (see FIG. 10) defined in the score calculation algorithm linked to the inspection item extracted in step S304 described above, Register (step S305).
  • FIG. 11 is a flowchart of the information processing method according to this embodiment
  • FIG. 12 is a diagram showing an example of the determination table 484 according to this embodiment.
  • the evaluation method according to this embodiment includes a plurality of steps from step S401 to step S403. Details of each step included in the estimation method according to the present embodiment will be described below.
  • the server 400 acquires the score (determination index) regarding the user's disease obtained by the series of processes in step S300 described above (step S401).
  • the server 400 extracts, for example, evaluation conditions linked to the inspection items extracted during score estimation from the determination table 484 shown in FIG. 12, and according to the extracted evaluation conditions, Evaluate whether the user needs a detailed examination or treatment (step S402). Specifically, the server 400 evaluates the disease state of the user based on the score obtained according to the rules defined in the determination table 484 (for example, comparing the score with a predetermined threshold). When the server 400 evaluates that the user's condition is abnormal, the server 400 determines that the user needs a detailed examination or treatment (step S402: Yes), and proceeds to the process of step S403 described later. On the other hand, when the user's disease state is evaluated as normal, the server 400 determines that the user does not need a detailed examination or treatment (step S402: No), and ends the process.
  • evaluation conditions linked to the inspection items extracted during score estimation from the determination table 484 shown in FIG. 12, and according to the extracted evaluation conditions, Evaluate whether the user needs a detailed examination or treatment (step S402).
  • the server 400
  • the server 400 notifies the medical staff, the user, and the user's family of an alarm that includes information that prompts detailed examination and information that prompts treatment (step S403).
  • the present embodiment it is possible to measure the user's actions in daily life and score the user's disease state without imposing a burden on the user. Furthermore, in the present embodiment, it is possible to quickly issue an alarm to medical staff, users, etc., based on the score obtained on a daily basis. As a result, according to this embodiment, it is possible to provide the user with an opportunity for early detection and early treatment.
  • the inspection item that can be linked to the action when the user grips the daily necessities 100 is extracted from the inspection item table 482 .
  • the present disclosure is not limited to such an estimation method, and for example, the score may be estimated by detecting a predetermined motion from the estimated movement trajectory of the daily necessities 100 .
  • the configuration of the information processing system 10 and the configuration of each device included in the information processing system 10 are the same as in the above-described first embodiment except for the following points. Descriptions of these are omitted here.
  • the estimating unit 434 of the server 400 detects a movement trajectory corresponding to a specific action (e.g., straight up) from the estimated movement trajectory of the daily necessities 100, and Based on the trajectory and information on the weight of the daily necessities 100 when held by the user, the user's disease score (determination index) is estimated.
  • a specific action e.g., straight up
  • FIG. 13 is a flowchart of an information processing method according to this embodiment.
  • the estimation method according to this embodiment includes a plurality of steps from step S311 to step S314. Details of each step included in the estimation method according to the present embodiment will be described below.
  • the server 400 estimates the weight of the daily necessities 100 held by the user (step S311).
  • the details are the same as step S301 of the information processing method of the first embodiment described with reference to FIG. 9, so detailed description is omitted here.
  • the server 400 estimates the movement trajectory of the daily necessities 100 moved by the user's actions based on sensing data including acceleration data and angular velocity data (step S312).
  • the details are the same as step S303 of the information processing method of the first embodiment described with reference to FIG. 9, so detailed description is omitted here.
  • the server 400 refers to, for example, movement trajectory information corresponding to a specific action (predetermined action) in the database stored in the storage unit 480 from the estimated movement trajectory of the daily necessities 100, A corresponding movement locus is detected (step S313).
  • a specific action predetermined action
  • FIGS. 1A and 1B operations as shown in FIGS. 1A and 1B are defined as specific operations.
  • the server 400 estimates the score (determination index) related to the user's disease based on the score calculation formula or the like linked to the specific action extracted in step S313 described above (step S314).
  • the present embodiment it is possible to measure the user's actions in daily life and score the user's disease state without imposing a burden on the user. Furthermore, according to the present embodiment, the score is calculated only when a movement trajectory corresponding to a specific action is detected, so an increase in load caused by processing can be suppressed.
  • Third embodiment of the present disclosure >> ⁇ 4.1 Overview>
  • the present disclosure is not limited to the estimation methods according to the first and second embodiments.
  • an algorithm created in advance by machine learning is used to estimate a score (determination index) related to a user's disease.
  • a score determination index
  • the user's disease state can be scored with high accuracy.
  • a third embodiment of the present disclosure that performs such estimation will be described below.
  • the configuration of the information processing system 10 and the configuration of each device included in the information processing system 10 are the same as those of the above-described first embodiment except for the following points. We will omit these descriptions.
  • the estimating unit 434 of the server 400 uses an algorithm created in advance by machine learning to obtain information on the weight of the daily necessities 100 when held by the user and sensing data indicating the movement trajectory of the daily necessities 100. Based on, the score (determination index) regarding the user's disease is estimated.
  • FIG. 14 is a flowchart of an information processing method according to this embodiment.
  • the estimation method according to this embodiment includes a plurality of steps from step S321 to step S323. Details of each step included in the estimation method according to the present embodiment will be described below.
  • the server 400 estimates the weight of the daily necessities 100 held by the user (step S321).
  • the details are the same as step S301 of the information processing method of the first embodiment described with reference to FIG. 9, so detailed description is omitted here.
  • the server 400 estimates the movement trajectory of the daily necessities 100 moved by the user's actions, based on sensing data including acceleration data and angular velocity data (step S322).
  • the details are the same as step S303 of the information processing method of the first embodiment described with reference to FIG. 9, so detailed description is omitted here.
  • the server 400 uses an algorithm created in advance by machine learning to directly detect the disease of the user based on information on the weight of the daily necessities 100 when held by the user and information on the movement trajectory of the daily necessities 100 . is estimated (step S323).
  • the server 400 is based on sensing data including information on the weight of the daily necessities 100 held by the user and acceleration data and angular velocity data. may be used to directly estimate the user's disease score.
  • FIG. 15 is an explanatory diagram for explaining this embodiment.
  • the server 400 or another information processing device receives information on the weight of the daily necessities 100 held by the user, information on the movement trajectory of the daily necessities 100, etc. performs machine learning on the learner 440 of Specifically, as shown in FIG. 15, the server 400 or other information processing device is assumed to have a supervised learner 440 such as support vector regression or deep neural network. Learning device 440 stores information based on sensing data acquired from sensor unit 200 (weight information on daily necessities 100 when held by a user and information on the movement trajectory of daily necessities 100), and a specialist doctor for the user. A score (judgment index) relating to the user's disease, which is the result of diagnosis, is input as an input signal and a teacher signal. The learner 4440 performs machine learning on the relationship between these pieces of information according to predetermined rules.
  • a supervised learner 440 such as support vector regression or deep neural network.
  • Learning device 440 stores information based on sensing data acquired from sensor unit 200 (weight information on daily necessities 100 when held by a user and information on the movement trajectory
  • the learning device 440 receives a large number of pairs of teacher signals and input signals, and performs machine learning on these inputs to obtain weight information about the daily necessities 100 when held by the user, and An estimation model 488 indicating relationship information indicating the relationship between the information on the movement trajectory of the daily necessities 100 and the user's disease score can be constructed as the above algorithm. Further, sensing data including acceleration data and angular velocity data may be input to the learning device 440 instead of the information on the movement trajectory of the daily necessities 100 . Furthermore, user attribute information and the like may be input to the learning device 440, and such information can be used as information for grouping input targets and information for analyzing input targets. . Also, in this embodiment, the learner 440 may be a semi-supervised learner or a weakly supervised learner.
  • the present embodiment it is possible to measure the user's actions in daily life and score the user's disease state without imposing a burden on the user. Furthermore, according to the present embodiment, even if the user performs an action that is not similar to the action defined by the examination item or the like, the user's disease state can be scored with high accuracy.
  • FIG. 16 is a system diagram showing a schematic functional configuration of an information processing system 10a according to a modification of the embodiment.
  • an information processing system 10a includes a sensor unit 200a provided in a daily necessities 100, a user terminal 300, a server 400, and a medical staff, as in the first embodiment. and a user terminal 500, and further includes a camera 290.
  • the camera 290 is used to capture images of the daily necessities 100 to acquire the movement locus of the daily necessities 100 .
  • a plurality of cameras 290 are installed on the wall of the room where the user lives (for example, a hospital room). These cameras 290 capture images of the daily necessities 100 used by the user.
  • Server 400 analyzes the position information of these cameras 290 and the moving images of daily necessities 100 captured by each camera 290 to obtain the movement trajectory of daily necessities 100 carried and moved by the user.
  • a marker 102 having a characteristic shape and color is attached to the outer surface of the daily necessities 100 in order to facilitate the analysis.
  • the weight of daily necessities 100 can be estimated based on sensing data from weight sensor 220 of sensor unit 200, as in the first embodiment.
  • the information processing system 10 is not limited to the configuration example shown in FIG. 3 .
  • the information processing system 10 may include servers managed by various service providers that can utilize the user's disease state scores and evaluation results based on the scores described above.
  • An example of the configuration of an information processing system 10b according to this modified example will be described below with reference to FIG.
  • FIG. 17 is a system diagram showing a schematic functional configuration of an information processing system 10b according to this modification.
  • an information processing system 10b includes a server 600a managed by a life insurance company/health insurance association or the like, or a server 600a managed by a service providing company that provides users with services such as health food sales.
  • a server 600b and a server 600 managed by an analysis company that constructs databases and the like for developing pharmaceuticals and the like can be included.
  • a server 600a managed by a life insurance company/health insurance association or the like analyzes each user's score, evaluation results, attribute information, sensing data, etc. from the server 400, and identifies each user's insurance premiums and rewards.
  • the identification may involve the work, judgment, etc. of an employee of a life insurance company or a health insurance company.
  • the insurance premium may be reduced, a change to a limited plan, or the like may be performed.
  • the server 600a transmits the specified insurance premiums and rewards for each user to the user terminal 300 and the like.
  • a service provider that manages the server 600b provides users with wheelchairs, rehabilitation equipment, health food, health equipment, means of transportation for the user to visit hospitals, health applications that can be executed on the user terminal 300, and the like. Company.
  • the server 600b analyzes each user's score, evaluation result, attribute information, sensing data, etc. from the server 400, and specifies reward services for each user. Then, the server 600b transmits the content of the reward service identified for each user to the user terminal 300 or the like.
  • a server 600c managed by an analysis company can analyze each user's score, evaluation results, attribute information, sensing data, etc. from the server 400, and construct a database or the like for developing pharmaceuticals or the like. can.
  • Various information provided from the server 400 to the server 600c is preferably anonymized at the server 400. FIG. By doing so, it becomes impossible to specify the user associated with each piece of information, so the user's privacy can be protected.
  • the server 600c may transmit the constructed database or the like to the server 400 as information that can be used by the server 400.
  • the server 600c is not limited to constructing a database or the like for developing pharmaceuticals, etc., but also analyzes user groups for products such as health foods and health appliances, and data analysis for clinical development. you can go
  • server 600 may be performed by the server 400, or the server 600 may be configured as a device integrated with the server 400.
  • FIG. 18 is a block diagram showing a functional configuration example of the server 600 according to this modification.
  • the server 600 mainly includes a processing unit 630, an input unit 650, an output unit 660, a communication unit 670, and a storage unit 680, as shown in FIG.
  • Each functional unit of the server 600 will be described below.
  • the processing unit 630 can control each block of the server 600, and is implemented by hardware such as a CPU, ROM, and RAM, for example.
  • the input unit 650 can receive input of data and commands to the server 600, and is realized by, for example, a touch panel, a keyboard, and the like.
  • the output unit 660 is configured by, for example, a display or the like, and can output various kinds of information in the form of images or the like.
  • the communication unit 670 can transmit and receive information to and from an external device such as the server 400, and is implemented by a communication device such as a communication antenna, a transmission/reception circuit, or a port, for example.
  • the storage unit 680 can store programs, information, and the like for the processing unit 630 to execute various types of processing, and information obtained by the processing. It is implemented by a non-volatile memory or the like.
  • server 600 is not limited to the configuration example shown in FIG. 18, and may further include other functional units, for example.
  • the present disclosure is not limited to estimating a score (judgment index) related to a user's disease such as muscular dystrophy. It can be used when estimating a judgment index such as a disease. That is, according to the technology of the present disclosure, it can be said that it is possible to estimate the determination index regarding various user states. In this case, for example, by storing the table 490 of FIG. 19 showing an example of the table 490 according to the modified example of the embodiment of the present disclosure in the storage unit 480 of the server 400, the technology of the present disclosure can be used in various user states. It is possible to estimate a judgment index for
  • FIG. 20 is a block diagram showing an example of hardware configuration.
  • the server 400 will be described below as an example.
  • the user terminal 300, the medical staff terminal 500, and the server 600 can be explained in the same way.
  • Various types of processing by the server 400 are implemented by cooperation between software and hardware described below.
  • the server 400 has a CPU (Central Processing Unit) 901, a ROM (Read Only Memory) 902, a RAM (Random Access Memory) 903, and a host bus 904a.
  • the server 400 also has a bridge 904 , an external bus 904 b , an interface 905 , an input device 906 , an output device 907 , a storage device 908 , a drive 909 , a connection port 911 and a communication device 913 .
  • the server 400 may have a processing circuit such as a DSP (Digital Signal Processor) or an ASIC (Application Specific Integrated Circuit) in place of or in addition to the CPU 901 .
  • DSP Digital Signal Processor
  • ASIC Application Specific Integrated Circuit
  • the CPU 901 functions as an arithmetic processing device and a control device, and controls general operations within the server 400 according to various programs.
  • the CPU 901 may be a microprocessor.
  • the ROM 902 stores programs, calculation parameters, and the like used by the CPU 901 .
  • the RAM 903 temporarily stores programs used in the execution of the CPU 901, parameters that change as appropriate during the execution, and the like.
  • the CPU 901 can embody the processing unit 430 of the server 400, for example.
  • the CPU 901, ROM 902 and RAM 903 are interconnected by a host bus 904a including a CPU bus and the like.
  • the host bus 904a is connected via a bridge 904 to an external bus 904b such as a PCI (Peripheral Component Interconnect/Interface) bus.
  • PCI Peripheral Component Interconnect/Interface
  • host bus 904a, bridge 904 and external bus 904b need not necessarily have separate configurations from each other and may be implemented in a single configuration (eg, one bus).
  • the input device 906 is implemented by a device such as a mouse, keyboard, touch panel, button, microphone, switch, lever, etc., through which information is input by the practitioner.
  • the input device 906 may be, for example, a remote control device using infrared rays or other radio waves, or may be an external connection device such as a mobile phone or PDA (Personal Digital Assistant) compatible with the operation of the server 400.
  • the input device 906 may include, for example, an input control circuit that generates an input signal based on information input by the practitioner using the above input means and outputs the signal to the CPU 901 . By operating the input device 906, the practitioner can input various data to the server 400 and instruct processing operations.
  • the output device 907 is formed by a device capable of visually or audibly notifying the practitioner of the acquired information.
  • Such devices include display devices such as CRT (Cathode Ray Tube) display devices, liquid crystal display devices, plasma display devices, EL (Electro Luminescent) display devices and lamps, acoustic output devices such as speakers and headphones, and printer devices. etc.
  • the storage device 908 is a device for storing data.
  • the storage device 908 is realized by, for example, a magnetic storage device such as a HDD (Hard Disk Drive), a semiconductor storage device, an optical storage device, a magneto-optical storage device, or the like.
  • the storage device 908 may include a storage medium, a recording device that records data on the storage medium, a reading device that reads data from the storage medium, a deletion device that deletes data recorded on the storage medium, and the like.
  • the storage device 908 stores programs executed by the CPU 901, various data, and various data acquired from the outside.
  • the storage device 908 can embody the storage unit 480 of the server 400, for example.
  • the drive 909 is a reader/writer for storage media, and is built into the server 400 or externally attached.
  • the drive 909 reads information recorded on a removable storage medium such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, and outputs the information to the RAM 903 .
  • Drive 909 can also write information to a removable storage medium.
  • connection port 911 is an interface connected to an external device, and is a connection port with an external device capable of data transmission by, for example, USB (Universal Serial Bus).
  • USB Universal Serial Bus
  • the communication device 913 is, for example, a communication interface formed by a communication device or the like for connecting to the network 920 .
  • the communication device 913 is, for example, a communication card for wired or wireless LAN (Local Area Network), LTE (Long Term Evolution), Bluetooth (registered trademark), or WUSB (Wireless USB).
  • the communication device 913 may be a router for optical communication, a router for ADSL (Asymmetric Digital Subscriber Line), a modem for various types of communication, or the like.
  • the communication device 913 can transmit and receive signals to and from the Internet and other communication devices in accordance with a predetermined protocol such as TCP/IP (Transmission Control Protocol/Internet Protocol).
  • the communication device 913 can embody the communication unit 470 of the server 400, for example.
  • the network 920 is a wired or wireless transmission path for information transmitted from devices connected to the network 920 .
  • the network 920 may include a public network such as the Internet, a telephone network, a satellite communication network, various LANs (Local Area Networks) including Ethernet (registered trademark), WANs (Wide Area Networks), and the like.
  • Network 920 may also include a dedicated line network such as IP-VPN (Internet Protocol-Virtual Private Network).
  • the above-described embodiments of the present disclosure include, for example, an information processing method executed by an information processing apparatus or an information processing system as described above, a program for operating the information processing apparatus, and a program in which the program is recorded. may include non-transitory tangible media that have been processed. Also, the program may be distributed via a communication line (including wireless communication) such as the Internet.
  • each step in the information processing method according to the embodiment of the present disclosure described above does not necessarily have to be processed in the described order.
  • each step may be processed in an appropriately changed order.
  • each step may be partially processed in parallel or individually instead of being processed in chronological order.
  • the processing of each step does not necessarily have to be processed in accordance with the described method, and may be processed by another method by another functional unit, for example.
  • each component of each device illustrated is functionally conceptual and does not necessarily need to be physically configured as illustrated.
  • the specific form of distribution and integration of each device is not limited to the one shown in the figure, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured.
  • the present technology can also take the following configuration.
  • the information processing device Acquiring first sensing data resulting from the movement of the daily necessities by the user's actions from a first sensor provided on the daily necessities held by the user; estimating a judgment index related to the user's disease based on the first sensing data and weight information related to the daily necessities when held by the user; including, Information processing methods.
  • the article includes at least one of tableware, cooking utensils, toothbrushes, hairbrushes, dryers, towels, shoehorns, clothes, hats, bags, writing utensils, tools, mobile terminals, and furniture. Information processing method described.
  • the article is an article whose weight relative to the daily necessities when held by the user can change due to a change in the amount of the object contained in the article.
  • the article includes at least one of a glass, a cup, a plate, a water bottle, a pot, a kettle, and a bag.
  • the first sensor includes at least one of an acceleration sensor, an angular velocity sensor, a geomagnetic sensor, and an atmospheric pressure sensor.
  • the information processing device further includes estimating a movement trajectory of the daily item held by the user based on the first sensing data, The determination index is estimated based on the movement trajectory and information on the weight of the daily item when held by the user.
  • the information processing method according to any one of (1) to (6) above.
  • the information processing device pre-storing information on a plurality of test items for testing the disease state; extracting, from among the plurality of inspection items, the inspection item that can be linked to the motion of the user based on the movement trajectory and information on the weight of the daily item when held by the user; , estimating the judgment index based on the extracted information about the inspection item; including, The information processing method according to (7) above.
  • the information processing device detecting a predetermined motion from the movement trajectory; estimating the determination index based on the detected movement trajectory in the predetermined motion and information on the weight of the daily item when held by the user; including, The information processing method according to (7) above.
  • the information processing device uses an algorithm created in advance by machine learning to determine the disease of the user based on the first sensing data and information on the weight of the daily item when held by the user. estimating an indicator; including, The information processing method according to any one of (1) to (6) above. (11) Any one of (1) to (10) above, wherein the information processing device estimates weight information about the daily item when held by the user, based on the first sensing data.
  • the information processing device Acquiring second sensing data from a second sensor provided in the daily necessities; estimating the weight of the daily necessities when held by the user based on the second sensing data;
  • the information processing method according to any one of (1) to (10) above, including (13) The information processing method according to (12) above, wherein the second sensor includes at least one of a pressure sensor, a resistance sensor, a vibration sensor, a weight sensor, and a photoreflector sensor.
  • the information processing device pre-storing information about the weight of the daily necessities in association with the identification information of the daily necessities; Acquiring the identification information together with the first sensing data; estimating weight information about the daily necessities when held by the user based on the acquired identification information;
  • the information processing method according to any one of (1) to (10) above including (15) The information processing device Evaluating the disease state of the user based on the estimated judgment index related to the user's disease to generate an evaluation result; outputting the evaluation result;
  • the information processing device storing the estimated decision indicator; Evaluating the user's disease state based on the change over time of the decision index;
  • the information processing method according to (15) above comprising: (17) The information processing method according to (15) above, wherein the information processing device outputs the evaluation result to an external terminal.
  • an acquisition unit that acquires first sensing data generated from movement of the daily necessities by the user's motion from a first sensor provided on the daily necessities held by the user; an estimating unit for estimating a judgment index related to the user's disease based on the first sensing data and weight information related to the daily necessities when held by the user; comprising Information processing equipment.
  • the computer an acquisition unit that acquires first sensing data generated from movement of the daily necessities by the user's motion from a first sensor provided on the daily necessities held by the user; an estimating unit for estimating a judgment index related to the user's disease based on the first sensing data and weight information related to the daily necessities when held by the user;
  • a program that functions as (20) a first sensor attached to a daily item held by a user and acquiring first sensing data generated from movement of the daily item by the user's motion; an information processing device for estimating a judgment index related to the user's disease based on the first sensing data and weight information related to the daily necessities when held by the user; including, Information processing system.
  • the information processing device Acquiring first sensing data resulting from the movement of the daily necessities by the action of the user from a first sensor provided on the daily necessities held by the user; estimating a judgment index relating to the user's physical condition based on the first sensing data and information about the weight of the daily item when held by the user; including, Information processing methods.

Abstract

Provided is an information processing method including: a feature in which an information processing device (400) acquires, from a first sensor (210) provided to a commodity (100) grasped by a user, first sensing data generated from movement of the commodity due to the action of the user; and a feature in which the information processing device (400) estimates an assessment index relating to a disease of the user on the basis of the first sensing data and weight information relating to the commodity while the commodity is grasped by the user.

Description

情報処理方法、情報処理装置、プログラム、及び、情報処理システムInformation processing method, information processing device, program, and information processing system
　本開示は、情報処理方法、情報処理装置、プログラム、及び、情報処理システムに関する。 The present disclosure relates to an information processing method, an information processing device, a program, and an information processing system.
　筋ジストロフィー等のような進行性の疾患については、重症化の予兆を早期に発見して、早期治療・リハビリを行うことが進行を遅らせるために有効であるとされている。そして、早期発見のために、客観的に患者（ユーザ）の筋機能を計測する計測手法が確立されている。計測手法としては、例えば、患者の利き手に所定の重さを持つ物体を持たせて所定の動作をさせたりして、その動作の状態に基づいて患者の筋機能の評価を行うというものである。 For progressive diseases such as muscular dystrophy, early detection of signs of aggravation and early treatment and rehabilitation are said to be effective in slowing the progression. For early detection, a measurement method for objectively measuring a patient's (user's) muscle function has been established. As a measurement method, for example, the patient's dominant hand is made to hold an object with a predetermined weight and make a predetermined movement, and the patient's muscle function is evaluated based on the state of that movement. .
特表２０１９－５３１５６９号公報Japanese Patent Publication No. 2019-531569
　しかしながら、上記計測手法は、一般的には専門医により実施されるため、高頻度で実施することが難しいことから、早期発見、早期治療の機会を逃してしまうことがある。そこで、患者（ユーザ）の身体の一部に計測器（モーション・キャプチャ等）を装着し、日常的に患者の動作を計測することも考えられるが、毎日装着することや、意識して所定の動作を行う必要があることから、患者への負担が大きい。 However, since the above measurement method is generally performed by specialists, it is difficult to perform it frequently, which may lead to missed opportunities for early detection and early treatment. Therefore, it is conceivable to attach a measuring device (motion capture, etc.) to a part of the patient's (user's) body and measure the patient's movements on a daily basis. Since it is necessary to perform an operation, the burden on the patient is large.
　そこで、本開示では、ユーザに負担をかけることなく、日常生活の中でユーザの動作を計測し、ユーザの状態をスコア化することが可能な情報処理方法、情報処理装置、プログラム、及び、情報処理システムを提案する。 Therefore, in the present disclosure, an information processing method, an information processing device, a program, and an information processing method capable of measuring a user's behavior in daily life and scoring the user's state without imposing a burden on the user. Propose a treatment system.
　本開示によれば、情報処理装置が、ユーザが把持する日用品に具設された第１のセンサから、当該ユーザの動作による前記日用品の移動から生じる第１のセンシングデータを取得することと、前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定することと、を含む、情報処理方法が提供される。 According to the present disclosure, the information processing device acquires first sensing data generated from movement of the daily necessities by the user's actions from a first sensor provided on the daily necessities held by the user; An information processing method is provided, including estimating a judgment index related to the user's disease based on first sensing data and weight information related to the daily item when held by the user.
　また、本開示によれば、ユーザが把持する日用品に具設された第１のセンサから、当該ユーザの動作による前記日用品の移動から生じる第１のセンシングデータを取得する取得部と、前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定する推定部と、を備える、情報処理装置が提供される。 Further, according to the present disclosure, an acquisition unit that acquires first sensing data generated from movement of the daily necessities by the user's action from a first sensor provided on the daily necessities held by the user; and an estimating unit for estimating a judgment index related to the user's disease, based on sensing data of and weight information related to the daily necessities when held by the user.
　また、本開示によれば、コンピュータを、ユーザが把持する日用品に具設された第１のセンサから、当該ユーザの動作による前記日用品の移動から生じる第１のセンシングデータを取得する取得部と、前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定する推定部と、として機能させる、プログラムが提供される。 Further, according to the present disclosure, an acquisition unit configured to acquire first sensing data generated from movement of the daily necessities by the user's actions from a first sensor provided on the daily necessities held by the user, and A program is provided that functions as an estimating unit that estimates a judgment index regarding the user's disease based on the first sensing data and weight information regarding the daily item when the user holds it. .
　さらに、本開示によれば、ユーザが把持する日用品に具設され、当該ユーザの動作による前記日用品の移動から生じる第１のセンシングデータを取得する第１のセンサと、前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定する情報処理装置と、を含む、情報処理システムが提供される。 Further, according to the present disclosure, a first sensor that is provided in a daily item held by a user and that acquires first sensing data resulting from movement of the daily item by the user's action; the first sensing data; and an information processing device for estimating a judgment index related to the user's disease based on weight information related to the daily item when held by the user.
筋ジストロフィーの検査例を説明するための説明図である。It is an explanatory view for explaining an example of a test of muscular dystrophy. 筋ジストロフィーの検査例を説明するための表である。FIG. 11 is a table for explaining an example of muscular dystrophy examination; FIG. 本開示の実施形態で使用される日用品１００の一例を説明するための説明図（その１）である。1 is an explanatory diagram (1) for explaining an example of a daily necessities 100 used in an embodiment of the present disclosure; FIG. 本開示の実施形態で使用される日用品１００の一例を説明するための説明図（その２）である。FIG. 2 is an explanatory diagram (Part 2) for explaining an example of the daily necessities 100 used in the embodiment of the present disclosure; 本開示の実施形態で使用される日用品１００の一例を説明するための説明図（その３）である。FIG. 3 is an explanatory diagram (part 3) for explaining an example of the daily necessities 100 used in the embodiment of the present disclosure; 本開示の第１の実施形態に係る情報処理システム１０の概略的な機能構成を示したシステム図である。1 is a system diagram showing a schematic functional configuration of an information processing system 10 according to a first embodiment of the present disclosure; FIG. 本開示の第１の実施形態に係るセンサユニット２００の機能構成例を示すブロック図である。2 is a block diagram showing a functional configuration example of a sensor unit 200 according to the first embodiment of the present disclosure; FIG. 本開示の第１の実施形態に係るユーザ端末３００の機能構成例を示すブロック図である。2 is a block diagram showing an example functional configuration of a user terminal 300 according to the first embodiment of the present disclosure; FIG. 本開示の第１の実施形態に係るサーバ４００の機能構成例を示すブロック図である。2 is a block diagram showing a functional configuration example of a server 400 according to the first embodiment of the present disclosure; FIG. 本開示の第１の実施形態に係る医療従事者用端末５００の機能構成例を示すブロック図である。2 is a block diagram showing a functional configuration example of a terminal for medical staff 500 according to the first embodiment of the present disclosure; FIG. 本開示の第１の実施形態に係る情報処理方法のシーケンス図である。1 is a sequence diagram of an information processing method according to the first embodiment of the present disclosure; FIG. 本開示の第１の実施形態に係る情報処理方法のフローチャート（その１）である。1 is a flowchart (part 1) of an information processing method according to the first embodiment of the present disclosure; 本開示の第１の実施形態に係る検査項目テーブル４８２の一例を示す図である。FIG. 4 is a diagram showing an example of an inspection item table 482 according to the first embodiment of the present disclosure; FIG. 本開示の第１の実施形態に係る情報処理方法のフローチャート（その２）である。2 is a flowchart (part 2) of an information processing method according to the first embodiment of the present disclosure; 本開示の第１の実施形態に係る判定テーブル４８４の一例を示す図である。4 is a diagram showing an example of a determination table 484 according to the first embodiment of the present disclosure; FIG. 本開示の第２の実施形態に係る情報処理方法のフローチャートである。6 is a flow chart of an information processing method according to a second embodiment of the present disclosure; 本開示の第３の実施形態に係る情報処理方法のフローチャートである。8 is a flowchart of an information processing method according to a third embodiment of the present disclosure; 本開示の第３の実施形態を説明するための説明図である。FIG. 11 is an explanatory diagram for describing a third embodiment of the present disclosure; FIG. 本開示の実施形態の変形例に係る情報処理システム１０ａの概略的な機能構成を示したシステム図である。FIG. 10 is a system diagram showing a schematic functional configuration of an information processing system 10a according to a modified example of the embodiment of the present disclosure; 本開示の実施形態の変形例に係る情報処理システム１０ｂの概略的な機能構成を示したシステム図である。FIG. 10 is a system diagram showing a schematic functional configuration of an information processing system 10b according to a modified example of the embodiment of the present disclosure; 本開示の実施形態の変形例に係るサーバ６００の機能構成例を示すブロック図である。FIG. 11 is a block diagram showing a functional configuration example of a server 600 according to a modified example of the embodiment of the present disclosure; FIG. 本開示の実施形態の変形例に係るテーブル４９０の一例を示す図である。FIG. 11 is a diagram showing an example of a table 490 according to a modified example of the embodiment of the present disclosure; FIG. ハードウェア構成の例を示すブロック図である。3 is a block diagram showing an example of hardware configuration; FIG.
　以下に、添付図面を参照しながら、本開示の好適な実施の形態について詳細に説明する。なお、本明細書及び図面において、実質的に同一の機能構成を有する構成要素については、同一の符号を付することにより重複説明を省略する。また、本明細書及び図面において、実質的に同一又は類似の機能構成を有する複数の構成要素を、同一の符号の後に異なるアルファベットを付して区別する場合がある。ただし、実質的に同一又は類似の機能構成を有する複数の構成要素の各々を特に区別する必要がない場合、同一符号のみを付する。 Preferred embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In the present specification and drawings, constituent elements having substantially the same functional configuration are denoted by the same reference numerals, thereby omitting redundant description. In addition, in this specification and drawings, a plurality of components having substantially the same or similar functional configuration may be distinguished by attaching different alphabets after the same reference numerals. However, when there is no particular need to distinguish between a plurality of components having substantially the same or similar functional configurations, only the same reference numerals are used.
　なお、説明は以下の順序で行うものとする。
１．　本開示の実施形態を創作するに至る背景
　　　１．１　背景
　　　１．２　概要
２．　本開示の第１の実施形態
　　　２．１　情報処理システム
　　　２．２　日用品
　　　２．３　センサユニット
　　　２．４　ユーザ端末
　　　２．５　サーバ
　　　２．６　医療従事者用端末
　　　２．７　情報処理方法
３．　本開示の第２の実施形態
　　　３．１　概要
　　　３．２　情報処理方法
４．　本開示の第３の実施形態
　　　４．１　概要
　　　４．２　情報処理方法
５．　変形例
　　　５．１　変形例１
　　　５．２　変形例２
　　　５．３　変形例３
６．　まとめ
７．　ハードウェア構成の例
８．　補足 Note that the description will be given in the following order.
1. Background leading to creation of embodiments of the present disclosure 1.1 Background 1.2 Overview 2. First embodiment of the present disclosure 2.1 Information processing system 2.2 Daily necessities 2.3 Sensor unit 2.4 User terminal 2.5 Server 2.6 Terminal for medical staff 2.7 Information processing method 3. Second embodiment of the present disclosure 3.1 Overview 3.2 Information processing method 4. Third embodiment of the present disclosure 4.1 Overview 4.2 Information processing method5. Modification 5.1 Modification 1
5.2 Modification 2
5.3 Modification 3
6. Summary 7. Example of hardware configuration8. supplement
　＜＜１．　本開示の実施形態を創作するに至る背景＞＞
　＜１．１　背景＞
　まずは、本開示の実施形態を説明する前に、図１Ａ及び図１Ｂを参照して、本発明者が本開示の実施形態を創作するに至る背景について説明する。図１Ａは、筋ジストロフィーの検査例を説明するための説明図であり、図１Ｂは、筋ジストロフィーの検査例を説明するための表である。 <<1. Background leading to the creation of the embodiments of the present disclosure>>
<1.1 Background>
First, before describing the embodiments of the present disclosure, the background leading to the creation of the embodiments of the present disclosure by the inventor will be described with reference to FIGS. 1A and 1B. FIG. 1A is an explanatory diagram for explaining an example of testing for muscular dystrophy, and FIG. 1B is a table for explaining an example of testing for muscular dystrophy.
　まずは、筋ジストロフィーについて説明する。筋ジストロフィーとは、骨格筋の壊死／再生を主病変とする遺伝性筋疾患の総称であり、いずれも筋肉に不可欠なタンパク質の生成に関する遺伝子に変異が生じたためにおきる病気である。遺伝子の変異によりタンパク質の機能に障害が生じた場合には、細胞の正常な機能を維持できなくなるため、筋肉の変形や壊死が発生し、その結果、筋力が低下することから、運動機能障害等の症状に至ることとなる。また、筋ジストロフィーの症状は、骨間筋障害による運動機能低下が主なものとされているが、拘縮（関節が硬くなって可動域が狭くなる）、骨粗鬆症、呼吸機能障害、心筋障害、嚥下機能障害、骨代謝異常、中枢神経障害等の様々な機能障害や合併症を伴うことがある。 First, let me explain about muscular dystrophy. Muscular dystrophy is a general term for hereditary muscle diseases whose main lesion is necrosis/regeneration of skeletal muscle, and all of these diseases are caused by mutations in genes related to the production of proteins essential for muscles. When protein function is impaired due to gene mutation, normal cell function cannot be maintained, resulting in muscle deformation and necrosis, resulting in decreased muscle strength and motor dysfunction. symptoms. The main symptoms of muscular dystrophy are decreased motor function due to interosseous muscle damage, but contractures (joints become stiff and range of motion narrows), osteoporosis, respiratory dysfunction, myocardial dysfunction, and swallowing. It may be accompanied by various functional disorders and complications such as functional disorders, bone metabolism disorders, and central nervous system disorders.
　さらに、筋ジストロフィーは進行性の疾患であり、重症化の予兆を早期に発見して、早期治療・リハビリを行うことが進行を遅らせるために有効であるとされている。そして、早期発見のために、客観的に患者の筋機能を計測する計測手法が重要となる。計測手法としては、例えば、上肢筋機能（Ｐｅｒｆｏｒｍａｎｃｅ　ｏｆ　Ｕｐｐｅｒ　Ｌｉｍｂ：ＰＵＬ）障害度分類（９段階法）といった手法が用いられている。この手法は、図１Ａ及び図１Ｂに示すように、患者の利き手に所定の重さを持つ物体を持たせて所定の動作をさせたり、上記物体を持たせることなく所定の動作をさせたりして、その動作の状態に基づいて患者の筋機能の評価を行うというものである。 In addition, muscular dystrophy is a progressive disease, and early detection of signs of aggravation and early treatment and rehabilitation are said to be effective in slowing progression. For early detection, a measurement method that objectively measures the muscle function of patients is important. As a measurement method, for example, a method such as the Performance of Upper Limb (PUL) disability degree classification (9-step method) is used. In this method, as shown in FIGS. 1A and 1B, the dominant hand of the patient is allowed to hold an object having a predetermined weight and perform a predetermined motion, or the patient's dominant hand is allowed to perform a predetermined motion without holding the object. Then, the patient's muscle function is evaluated based on the state of the movement.
　しかしながら、このような手法は、一般的には専門医により計測、評価されることから、患者の通院が必要となり、計測の実施は、例えば数か月に１回といった頻度となってしまうことがある。さらに、昨今の感染症が拡大している状況下では、感染症の防止の観点から、患者が定期的に通院することは難しいこともあり、やはり計測の実施の頻度を高めることが難しい。その結果、症状の進行の度合いを高頻度で計測することが難しいことから、早期発見、早期治療の機会を逃してしまうことがある。 However, since such a method is generally measured and evaluated by a medical specialist, it is necessary for the patient to go to the hospital, and the frequency of measurement may be, for example, once every several months. . Furthermore, in the current situation where infectious diseases are spreading, it is difficult for patients to visit the hospital regularly from the viewpoint of preventing infectious diseases, and it is also difficult to increase the frequency of measurement implementation. As a result, it is difficult to measure the degree of progression of symptoms at high frequency, and opportunities for early detection and early treatment may be missed.
　そこで、患者の身体の一部に計測器（モーション・キャプチャ等）を装着し、日常的に患者の動作を計測することも考えられるが、毎日装着することや、意識して所定の動作を行う必要があることから、患者への負担が大きい。 Therefore, it is conceivable to wear a measuring device (motion capture, etc.) on a part of the patient's body and measure the patient's movement on a daily basis, but it is also possible to wear it every day or consciously perform a predetermined movement. Since it is necessary, the burden on the patient is large.
　＜１．２　概要＞
　本発明者は、このような状況を鑑みて、早期発見、早期治療の機会を患者に提供すべく、日常生活の中で患者に負担をかけることなく症状の進行の度合いを計測することができないかと、鋭意検討を進めていた。そのような中、本発明者は、患者の身体の一部に計測器を装着するのではなく、患者が日常的に使用する日用品に計測器を具設することを独自に着想し、本開示の実施形態を創作するに至った。本開示の実施形態によれば、日常的な習慣性の高い行動における日用品を把持する患者の動作を、当該日用品に具設された計測器によってモニタリングすることにより、患者に負担をかけることなく、症状の進行の度合いを計測することが可能となる。 <1.2 Overview>
In view of this situation, the present inventors, in order to provide patients with opportunities for early detection and early treatment, cannot measure the degree of progression of symptoms in daily life without burdening the patient. I was seriously considering it. Under such circumstances, the present inventor independently conceived of providing a measuring instrument to a daily item that a patient uses on a daily basis, rather than attaching the measuring instrument to a part of the patient's body. I came to create an embodiment of. According to an embodiment of the present disclosure, by monitoring a patient's action of holding a daily item in a highly addictive daily action with a measuring instrument provided in the daily item, the patient is not burdened. It becomes possible to measure the degree of progression of symptoms.
　以下、本発明者が創作した本開示の実施形態の概要を、図２Ａから図２Ｃを参照して説明する。図２Ａから図２Ｃは、本開示の実施形態で使用される日用品１００の一例を説明するための説明図である。 An outline of an embodiment of the present disclosure created by the inventor will be described below with reference to FIGS. 2A to 2C. 2A to 2C are explanatory diagrams for explaining an example of daily necessities 100 used in the embodiment of the present disclosure.
　近年、慣性計測ユニット（Ｉｎｅｒｔｉａｌ　Ｍｅａｓｕｒｅｍｅｎｔ　Ｕｎｉｔ：ＩＭＵ）の小型化が進んだことから、様々な物品に具設することが可能となった。そこで、本発明者は、患者が手で把持して使用するアイテムである日用品に、ＩＭＵを具設することを独自に着想した。例えば、日用品としては、食器（グラス、コップ、お椀、皿、フォーク、スプーン、ナイフ等）、調理道具（包丁等）、歯ブラシ、ヘアブラシ、ドライヤー、タオル、靴べら、洋服、帽子、カバン、筆記用具、工具、携帯端末、及び、家具（椅子等）を挙げることができる。また、日用品に搭載されるＩＭＵは、患者の動作による日用品の移動から変化が生じる３軸加速度、３軸角速度等を検出するセンサであることから、ＩＭＵから出力されるセンシングデータに基づいて、日用品の移動軌跡を推定することができる。さらに、推定された日用品の移動軌跡から、当該日用品を把持する患者の手の動作を計測することができる。 In recent years, the miniaturization of Inertial Measurement Units (IMUs) has made it possible to install them on various items. Therefore, the inventor of the present invention originally came up with the idea of providing an IMU to daily necessities, which are items that are held by a patient's hand. For example, daily necessities include tableware (glasses, cups, bowls, plates, forks, spoons, knives, etc.), cooking utensils (knives, etc.), toothbrushes, hairbrushes, dryers, towels, shoehorns, clothes, hats, bags, writing utensils, Examples include tools, mobile terminals, and furniture (chairs, etc.). In addition, since the IMU mounted on daily necessities is a sensor that detects three-axis acceleration, three-axis angular velocity, etc. that change due to the movement of daily necessities due to the movement of the patient, based on the sensing data output from the IMU, can be estimated. Furthermore, the motion of the patient's hand holding the daily necessities can be measured from the estimated movement trajectory of the daily necessities.
　例えば、図２Ａには、日用品１００ａとしてコップが図示されており、コップの所定の点を基準点とするＸ軸、Ｙ軸、Ｚ軸が定められ、ＩＭＵによりこれら３軸の加速度及び角速度を計測することができる。また、同様に、図２Ｂには、日用品１００ｂとしてカップが図示されており、図２Ｃには、日用品１００ｃとしてスプーンが図示されている。 For example, FIG. 2A shows a cup as the daily article 100a, and the X, Y, and Z axes are defined with a predetermined point on the cup as a reference point, and the IMU measures the acceleration and angular velocity of these three axes. can do. Similarly, FIG. 2B illustrates a cup as daily necessities 100b, and FIG. 2C illustrates a spoon as daily necessities 100c.
　このように、本発明者が創作した本開示の実施形態においては、患者が日常的に使用する日用品１００にＩＭＵを具設することにより、患者に負担をかけることなく、日常生活の中で患者の動作を計測することができる。そして、本開示の実施形態においては、患者の動作を計測し、計測結果に基づき患者の疾患の状態をスコア化し、スコアに応じてユーザの疾患の状態を評価する。さらに、本開示の実施形態においては、評価結果に基づいて必要と判断された場合には、医療従事者、ユーザ、及びユーザの家族に精密検査のためにユーザに通院を促すためのアラート等を自動送信する。その結果、本実施形態によれば、早期発見、早期治療の機会を患者に提供することが可能となる。 In this way, in the embodiment of the present disclosure created by the present inventor, by providing the IMU in the daily necessities 100 that the patient uses on a daily basis, the patient can use it in their daily life without burdening the patient. can be measured. Then, in the embodiment of the present disclosure, the patient's motion is measured, the patient's disease state is scored based on the measurement results, and the user's disease state is evaluated according to the score. Furthermore, in the embodiment of the present disclosure, if it is determined to be necessary based on the evaluation results, an alert or the like is sent to the medical staff, the user, and the user's family to encourage the user to go to the hospital for a detailed examination. Send automatically. As a result, according to this embodiment, it is possible to provide patients with an opportunity for early detection and early treatment.
　また、図１Ａ及び図１Ｂを参照して説明したように、筋機能の評価においては、患者は、所定の重量の物体を利き手に把持して、所定の動作を行うことが求められる。また、筋機能の計測手法によっては、患者は、様々な重量の物体を把持することが求められる場合もある。 In addition, as described with reference to FIGS. 1A and 1B, in the evaluation of muscle function, the patient is required to hold an object of a predetermined weight with the dominant hand and perform a predetermined action. In addition, depending on the muscle function measurement method, the patient may be required to grasp objects of various weights.
　図２Ａや図２Ｂに示すコップやカップといった日用品１００は、内包する液体（対象物）の量に応じて、日用品１００全体の重量が変化し得る。そこで、本開示の実施形態においては、このような全体の重量が変化し得る日用品１００にＩＭＵを具設することにより、様々な重量の物品を把持した際の患者の動作を計測することができる。詳細には、本開示の実施形態においては、全体の重量が変化し得る日用品１００に重量センサ等を具設することにより、当該日用品の内包する物体の重さを計測する。そして、本開示の実施形態においては、患者の動作による日用品１００の移動軌跡とともに、患者が担持した際の日用品１００の全体の重さの情報（詳細には、日用品１００自体の重さと日用品１００の内包する対象物の重さとの総和）に基づいて、患者の筋機能の状態を精度よくスコア化することができる。 The daily necessities 100 such as cups and cups shown in FIGS. 2A and 2B can change the weight of the entire daily necessities 100 according to the amount of liquid (object) contained therein. Therefore, in the embodiment of the present disclosure, by providing an IMU to such daily necessities 100 whose overall weight can change, it is possible to measure the patient's movements when gripping articles of various weights. . Specifically, in the embodiment of the present disclosure, by providing a weight sensor or the like in daily necessities 100 whose overall weight can change, the weight of objects included in the daily necessities is measured. In the embodiment of the present disclosure, along with the trajectory of movement of the daily necessities 100 by the patient's motion, information on the overall weight of the daily necessities 100 when carried by the patient (specifically, the weight of the daily necessities 100 itself and the weight of the daily necessities 100 The state of muscle function of the patient can be accurately scored based on the sum of the weights of the objects contained therein).
　なお、全体の重量が変化し得る日用品１００としては、グラス、コップ、皿、水筒、鍋、ヤカン、及び、カバン等を挙げることができる。もちろん、本開示の実施形態においては、図２Ｃに示すスプーンのような全体の重量が変化しない日用品１００を用いてもよい。本開示の実施形態においては、例えば、日常生活の中で、患者の周りに存在する様々な重量を持つ日用品１００を用いることで、様々な重量の物品を把持した際の患者の動作を計測することができる。以下、このような本開示の実施形態の詳細を順次説明する。 Examples of daily necessities 100 whose overall weight can change include glasses, cups, plates, water bottles, pots, kettles, and bags. Of course, an embodiment of the present disclosure may use a household item 100 whose overall weight does not change, such as the spoon shown in FIG. 2C. In the embodiment of the present disclosure, for example, daily necessities 100 with various weights that exist around the patient in daily life are used to measure the patient's movements when gripping articles of various weights. be able to. The details of such embodiments of the present disclosure will be sequentially described below.
　なお、以下の説明においては、日用品１００を把持する動作を計測される被測定者をユーザと呼ぶ。 It should be noted that in the following description, a person to be measured whose action of gripping daily necessities 100 is called a user.
　＜＜２．　本開示の第１の実施形態＞＞
　＜２．１　情報処理システム＞
　まずは、本開示の第１の実施形態に係る情報処理システム１０の構成の一例について、図３を参照して説明する。図３は、本開示の第１の実施形態に係る情報処理システム１０の概略的な機能構成を示したシステム図である。 <<2. First embodiment of the present disclosure >>
<2.1 Information processing system>
First, an example of the configuration of the information processing system 10 according to the first embodiment of the present disclosure will be described with reference to FIG. 3 . FIG. 3 is a system diagram showing a schematic functional configuration of the information processing system 10 according to the first embodiment of the present disclosure.
　図３に示すように、本実施形態に係る情報処理システム１０は、日用品１００に具設されたセンサユニット２００と、ユーザ端末３００と、サーバ（情報処理装置）４００と、医療従事者用端末５００とを主に含み、これらは互いにネットワーク８００を介して通信可能に接続される。詳細には、ユーザ端末３００、サーバ４００及び医療従事者用端末５００は、図示しない基地局等（例えば、携帯電話機の基地局、無線ＬＡＮ（Ｌｏｃａｌ　Ａｒｅａ　ｎｅｔｗｏｒｋ）のアクセスポイント等）を介してネットワーク８００に接続される。なお、ネットワーク８００で用いられる通信方式は、有線又は無線（例えば、ＷｉＦｉ（登録商標）、Ｂｌｕｅｔｏｏｔｈ（登録商標）等）を問わず任意の方式を適用することができるが、安定した動作を維持することができる通信方式を用いることが望ましい。以下に、本実施形態に係る情報処理システム１０の含まれる各装置の概要について説明する。 As shown in FIG. 3, the information processing system 10 according to the present embodiment includes a sensor unit 200 provided in a daily necessities 100, a user terminal 300, a server (information processing device) 400, and a medical staff terminal 500. , which are communicably connected to each other via a network 800 . Specifically, the user terminal 300, the server 400, and the medical staff terminal 500 are connected to the network 800 via a base station (for example, a mobile phone base station, a wireless LAN (Local Area network) access point, etc.) (not shown). connected to Note that the communication method used in the network 800 can be any method regardless of whether it is wired or wireless (for example, WiFi (registered trademark), Bluetooth (registered trademark), etc.), but stable operation is maintained. It is desirable to use a communication method that can Below, an outline of each device included in the information processing system 10 according to the present embodiment will be described.
　（センサユニット２００）
　センサユニット２００は、ユーザが日常的に使用する日用品１００に具設される。センサユニット２００は、少なくとも慣性計測ユニット（ＩＭＵ）（第１のセンサ）を含み、ユーザによって把持され、ユーザの動作によって移動する日用品１００の移動を計測することができる。さらに、センサユニット２００は、日用品１００がコップや皿等であった場合に、コップ内の液体や皿の上の固体の重量を計測するための重量センサ（第２のセンサ）等を含んでいてもよい。また、センサユニット２００は、後述するユーザ端末３００へ、近距離無線通信（例えば、ＷｉＦｉ（登録商標）、Ｂｌｕｅｔｏｏｔｈ（登録商標）等）を介して、センシングデータ等を送信することができる。なお、日用品１００及びセンサユニット２００の詳細については後述する。 (Sensor unit 200)
The sensor unit 200 is installed in the daily necessities 100 used by the user on a daily basis. The sensor unit 200 includes at least an inertial measurement unit (IMU) (first sensor), and can measure movement of the daily article 100 that is gripped by the user and moved by the user's action. Further, the sensor unit 200 includes a weight sensor (second sensor) and the like for measuring the weight of the liquid in the cup and the solid on the plate when the daily article 100 is a cup or a plate. good too. Further, the sensor unit 200 can transmit sensing data and the like to the user terminal 300, which will be described later, via short-range wireless communication (for example, WiFi (registered trademark), Bluetooth (registered trademark), etc.). Details of the daily necessities 100 and the sensor unit 200 will be described later.
　（ユーザ端末３００）
　ユーザ端末３００は、ユーザが日常的に使用する端末であって、センサユニット２００からのセンシングデータを後述するサーバ４００へ送信することができ、サーバ４００からの通知を受信することができる。例えば、ユーザ端末３００は、タブレット、スマートフォン、携帯電話、ラップトップ型ＰＣ（Ｐｅｒｓｏｎａｌ　Ｃｏｍｐｕｔｅｒ）、デスクトップ型ＰＣ、Ｈｅａｄ　Ｍｏｕｎｔｅｄ　Ｄｉｓｐｌａｙ（ＨＭＤ）等のデバイスであることができる。 (User terminal 300)
The user terminal 300 is a terminal that the user uses on a daily basis, and is capable of transmitting sensing data from the sensor unit 200 to the server 400 and receiving notifications from the server 400 . For example, the user terminal 300 can be a device such as a tablet, a smart phone, a mobile phone, a laptop PC (Personal Computer), a desktop PC, or a Head Mounted Display (HMD).
　また、本実施形態においては、ユーザ端末３００がタブレット、スマートフォン、携帯電話等であった場合には、ユーザ端末３００自体が、センサユニット２００が具設された日用品１００として機能することができる。さらに、本実施形態においては、センサユニット２００が、ユーザ端末３００の機能の一部（例えば、情報の送受信や出力等）を行ってもよく、この場合には、情報処理システム１０にユーザ端末３００が含まれていなくてもよい。なお、ユーザ端末３００の詳細については後述する。 Also, in the present embodiment, when the user terminal 300 is a tablet, smartphone, mobile phone, or the like, the user terminal 300 itself can function as the daily necessities 100 provided with the sensor unit 200. Furthermore, in the present embodiment, the sensor unit 200 may perform a part of the functions of the user terminal 300 (for example, transmission/reception and output of information). may not be included. Details of the user terminal 300 will be described later.
　（サーバ４００）
　サーバ４００は、センサユニット２００からユーザ端末３００を介して取得したセンシングデータ（第１のセンシングデータ）、及び、ユーザが把持した際の日用品１００に関する重さの情報に基づいて、ユーザの疾患に関するスコア（判断指標）を推定することができる。サーバ４００は、例えば、コンピュータ等により構成される。なお、サーバ４００の詳細構成については後述する。 (Server 400)
The server 400 provides a score for the user's disease based on sensing data (first sensing data) acquired from the sensor unit 200 via the user terminal 300 and information on the weight of the daily necessities 100 when held by the user. (determination index) can be estimated. The server 400 is configured by, for example, a computer. A detailed configuration of the server 400 will be described later.
　（医療従事者用端末５００）
　医療従事者用端末５００は、医師等の医療従事者が使用する端末であって、ネットワーク８００を介してサーバ４００からの通知を受信することができる。例えば、医療従事者用端末５００は、タブレット、スマートフォン、携帯電話、ラップトップ型ＰＣ、デスクトップ型ＰＣ等のデバイスであることができる。さらに、医療従事者用端末５００は、ユーザの家族が使用する端末であってもよい。なお、医療従事者用端末５００の詳細については後述する。 (Terminal 500 for medical staff)
Medical staff terminal 500 is a terminal used by a medical staff such as a doctor, and is capable of receiving notifications from server 400 via network 800 . For example, the medical worker terminal 500 can be a device such as a tablet, smart phone, mobile phone, laptop PC, desktop PC, or the like. Furthermore, the terminal for medical personnel 500 may be a terminal used by the user's family. Details of the medical staff terminal 500 will be described later.
　なお、図３においては、本実施形態に係る情報処理システム１０は、１対のセンサユニット２００とユーザ端末３００を含むものとして示されているが、本実施形態においてはこれに限定されるものではない。例えば、本実施形態に係る情報処理システム１０は、複数のセンサユニット２００と１つのユーザ端末３００との対を含んでいてもよい。さらに、本実施形態においては、複数のセンサユニット２００と１つ又は複数のユーザ端末３００とが対をなしていてもよい。また、実施形態に係る情報処理システム１０は、例えば、複数の医療従事者用端末５００や、センサユニット２００からユーザ端末３００へセンシングデータを送信する際の中継装置のような他の通信装置等を含んでもよい。 Although FIG. 3 shows the information processing system 10 according to this embodiment as including a pair of sensor units 200 and user terminals 300, this embodiment is not limited to this. do not have. For example, the information processing system 10 according to the present embodiment may include pairs of multiple sensor units 200 and one user terminal 300 . Furthermore, in this embodiment, a plurality of sensor units 200 and one or a plurality of user terminals 300 may be paired. Further, the information processing system 10 according to the embodiment includes, for example, a plurality of terminals 500 for medical staff, other communication devices such as a relay device for transmitting sensing data from the sensor unit 200 to the user terminal 300, and the like. may contain.
　＜２．２　日用品＞
　次に、センサユニット２００が具設される日用品１００について説明する。本実施形態に係る日用品１００は、日常生活の中で、ユーザが手で把持して使用する物品である。例えば、日用品１００は、先に説明したように、例えば、食器（グラス、コップ、お椀、皿、フォーク、スプーン、ナイフ等）、調理道具(包丁等)、歯ブラシ、ヘアブラシ、ドライヤー、タオル、靴べら、洋服、帽子、カバン、筆記用具、工具、携帯端末（スマートフォン、タブレット、携帯電話等）、及び、家具（椅子等）を挙げることができる。 <2.2 Daily necessities>
Next, the daily necessities 100 provided with the sensor unit 200 will be described. The daily necessities 100 according to the present embodiment are articles that are held by a user's hand and used in daily life. For example, as described above, the daily necessities 100 include tableware (glasses, cups, bowls, plates, forks, spoons, knives, etc.), cooking utensils (kitchen knives, etc.), toothbrushes, hairbrushes, dryers, towels, shoehorns, Clothes, hats, bags, writing utensils, tools, mobile terminals (smartphones, tablets, mobile phones, etc.), and furniture (chairs, etc.) can be mentioned.
　さらに、本実施形態においては、日用品１００は、内包する対象物の量に応じて、日用品１００全体の重量が変化し得る物品であることが好ましく、このような物品としては、例えば、グラス、コップ、皿、水筒、鍋、ヤカン、及び、カバン等を挙げることができる。 Furthermore, in the present embodiment, the daily necessities 100 are preferably articles whose overall weight can be changed according to the amount of objects contained therein. Examples of such articles include glasses and cups. , plates, water bottles, pots, kettles, and bags.
　＜２．３　センサユニット＞
　次に、本実施形態に係るセンサユニット２００の構成の一例について、図４を参照して説明する。図４は、本実施形態に係るセンサユニット２００の機能構成例を示すブロック図である。本実施形態に係るセンサユニット２００は、図４に示すように、ＩＭＵ（第１のセンサ）２１０と、重量センサ（第２のセンサ）２２０と、制御部２３０と、通信部２７０とを主に有する。以下に、センサユニット２００の有する各機能部について説明する。 <2.3 Sensor unit>
Next, an example of the configuration of the sensor unit 200 according to this embodiment will be described with reference to FIG. FIG. 4 is a block diagram showing a functional configuration example of the sensor unit 200 according to this embodiment. As shown in FIG. 4, the sensor unit 200 according to this embodiment mainly includes an IMU (first sensor) 210, a weight sensor (second sensor) 220, a control section 230, and a communication section 270. have. Each functional unit of the sensor unit 200 will be described below.
　（ＩＭＵ２１０）
　ＩＭＵ２１０は、日用品１００を把持するユーザの動作による当該日用品１００の移動から変化が生じる３軸加速度、３軸角速度等（第１のセンシングデータ）を検出するセンサである。具体的には、ＩＭＵ２１０は、加速度を取得する慣性センサである加速度センサ、角速度を取得する慣性センサであるジャイロセンサ（角速度センサ）等を含む。本実施形態においては、ＩＭＵ２１０は、上記慣性センサに代えて又は加えて、地磁気センサ、気圧センサ等を含んでもよい。 (IMU210)
The IMU 210 is a sensor that detects 3-axis acceleration, 3-axis angular velocity, and the like (first sensing data) that change due to movement of the daily item 100 by the action of the user holding the daily item 100 . Specifically, the IMU 210 includes an acceleration sensor that is an inertial sensor that acquires acceleration, a gyro sensor that is an inertial sensor that acquires angular velocity, and the like. In this embodiment, the IMU 210 may include a geomagnetic sensor, an atmospheric pressure sensor, etc. instead of or in addition to the inertial sensor.
　（重量センサ２２０）
　重量センサ２２０は、例えば、コップ等の日用品１００の内底面に具設されて、日用品１００が内包する液体や固体の重さを計測することができる。また、重量センサ２２０は、重量を直接的に計測するセンサであることに限定されるものではなく、日用品１００が内包する対象物の重さを推定することができるセンシングデータ（第２のセンシングデータ）を得られるセンサであってもよい。本実施形態においては、例えば、上記重量センサ２２０に変えて、圧力センサ、抵抗センサ（例えば、液体の抵抗値を計測する）、振動センサ（例えば、液体の振動を計測する）、フォトリフレクタセンサ（例えば、液体の入っている高さを計測する）等を用いてもよい。 (Weight sensor 220)
The weight sensor 220 is provided, for example, on the inner bottom surface of the daily necessities 100 such as a cup, and can measure the weight of the liquid or solid contained in the daily necessities 100 . Further, the weight sensor 220 is not limited to being a sensor that directly measures the weight, but sensing data (second sensing data) capable of estimating the weight of the object included in the daily necessities 100 ) may be used. In this embodiment, instead of the weight sensor 220, for example, a pressure sensor, a resistance sensor (for example, measuring the resistance value of liquid), a vibration sensor (for example, measuring vibration of liquid), a photoreflector sensor ( For example, measuring the height of the liquid) may be used.
　さらに、本実施形態においては、全体の重量が変化しない日用品１００である場合には、センサユニット２００に重量センサ２２０を設けていなくてもよい。さらに、本実施形態においては、ＩＭＵ２１０から得られる３軸加速度や３軸角速度等のセンシングデータから、日用品１００が内包する対象物の重さを推定することができる場合には、センサユニット２００に重量センサ２２０を設けていなくてもよい。後者の場合には、例えば、３軸加速度や３軸角速度等のセンシングデータから液体に入ったコップ等の振動が推定できる場合には、推定された振動からコップ内の液体の重さを推定してもよい。 Furthermore, in the present embodiment, the weight sensor 220 may not be provided in the sensor unit 200 if the daily product 100 does not change in overall weight. Furthermore, in the present embodiment, if the weight of an object included in the daily necessities 100 can be estimated from sensing data such as triaxial acceleration and triaxial angular velocity obtained from the IMU 210, the sensor unit 200 can detect the weight. The sensor 220 may not be provided. In the latter case, for example, if the vibration of a glass immersed in liquid can be estimated from sensing data such as three-axis acceleration and three-axis angular velocity, the weight of the liquid in the glass can be estimated from the estimated vibration. may
　（制御部２３０）
　制御部２３０は、所定の同期信号等に基づいてＩＭＵ２１０及び重量センサ２２０のセンシングデータの読み出し（サンプリング）タイミングを制御したり等、センサユニット２００における測定全般を制御することができる。また、制御部２３０は、図示しない記憶部をさらに有してもよく、当該記憶部には、ＩＭＵ２１０及び重量センサ２２０等を制御するための各種のパラメータ等が格納されていてもよい。さらに、制御部２３０は、ＩＭＵ２１０及び重量センサ２２０からのセンシングデータを時刻と紐づけてユーザ端末３００やサーバ４００へと出力するために、正確な時刻を把握する時計機構（図示省略）を内蔵してもよい。具体的には、制御部２３０は、例えば、ＣＰＵ（Ｃｅｎｔｒａｌ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）、ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）等により実現される。なお、制御部２３０の行う機能の一部又は全部は、後述するユーザ端末３００において実行されてもよい。 (control unit 230)
The control unit 230 can control overall measurement in the sensor unit 200, such as controlling reading (sampling) timing of sensing data of the IMU 210 and the weight sensor 220 based on a predetermined synchronization signal or the like. The control unit 230 may further have a storage unit (not shown), and the storage unit may store various parameters and the like for controlling the IMU 210, the weight sensor 220, and the like. Furthermore, the control unit 230 incorporates a clock mechanism (not shown) for grasping the accurate time in order to link the sensing data from the IMU 210 and the weight sensor 220 with the time and output it to the user terminal 300 and the server 400. may Specifically, the control unit 230 is realized by, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. Some or all of the functions performed by the control unit 230 may be performed by the user terminal 300, which will be described later.
　（通信部２７０）
　通信部２７０は、ユーザ端末３００等の外部装置との間で情報（例えば、センシングデータ）の送受信を行うことができる。言い換えると、通信部２７０は、データの送受信を行う機能を有する通信インタフェースと言える。具体的には、通信部２７０は、通信アンテナ、送受信回路やポート等の通信デバイスにより実現される。 (Communication unit 270)
The communication unit 270 can transmit and receive information (for example, sensing data) to and from an external device such as the user terminal 300 . In other words, the communication unit 270 can be said to be a communication interface having a function of transmitting and receiving data. Specifically, the communication unit 270 is realized by communication devices such as a communication antenna, a transmission/reception circuit, and a port.
　さらに、当該センサユニット２００は、上述のＩＭＵ２１０等の他にも、例えば、センサユニット２００内、又は、センサユニット２００と別体としてユーザの周囲に設けられた他のセンサを含んでいてもよい。他のセンサとしては、例えば、ＧＰＳ（Ｇｌｏｂａｌ　Ｐｏｓｉｔｉｏｎｉｎｇ　Ｓｙｓｔｅｍ）受信機、温度センサ、湿度センサ、ユーザの生体情報を取得する生体情報センサ（心拍センサ、血流センサ、血圧センサ、脳波センサ、体温センサ等）を挙げることができる。 Furthermore, the sensor unit 200 may include, for example, other sensors provided around the user in the sensor unit 200 or separately from the sensor unit 200, in addition to the above-described IMU 210 and the like. Other sensors include, for example, GPS (Global Positioning System) receivers, temperature sensors, humidity sensors, biological information sensors that acquire user's biological information (heartbeat sensor, blood flow sensor, blood pressure sensor, electroencephalogram sensor, body temperature sensor, etc. ) can be mentioned.
　また、センサユニット２００は、後述するユーザ端末３００と一体の装置として構成されてもよい。なお、本実施形態に係るセンサユニット２００は、図４に示される構成例に限定されるものではなく、例えば、他の機能部をさらに含んでいてもよい。 Also, the sensor unit 200 may be configured as a device integrated with the user terminal 300 described later. Note that the sensor unit 200 according to the present embodiment is not limited to the configuration example shown in FIG. 4, and may further include other functional units, for example.
　＜２．４　ユーザ端末＞
　次に、本実施形態に係るユーザ端末３００の構成の一例について、図５を参照して説明する。図５は、本実施形態に係るユーザ端末３００の機能構成例を示すブロック図である。本実施形態に係るユーザ端末３００は、図５に示すように、制御部３３０と、入力部３５０と、出力部３６０と、通信部３７０と、記憶部３８０とを主に有する。以下に、ユーザ端末３００の有する各機能部について説明する。 <2.4 User terminal>
Next, an example of the configuration of the user terminal 300 according to this embodiment will be described with reference to FIG. FIG. 5 is a block diagram showing a functional configuration example of the user terminal 300 according to this embodiment. The user terminal 300 according to this embodiment mainly includes a control unit 330, an input unit 350, an output unit 360, a communication unit 370, and a storage unit 380, as shown in FIG. Each functional unit of the user terminal 300 will be described below.
　（制御部３３０）
　制御部３３０は、ユーザ端末３００の各ブロックを制御することができる。当該制御部３３０は、例えば、ＣＰＵ、ＲＯＭ、ＲＡＭ等のハードウェアにより実現される。 (control unit 330)
The controller 330 can control each block of the user terminal 300 . The control unit 330 is implemented by hardware such as CPU, ROM, and RAM, for example.
　（入力部３５０）
　入力部３５０は、ユーザ端末３００へのデータ、コマンドの入力を受け付けることができる。より具体的には、当該入力部３５０は、タッチパネル、キーボード、マイクロフォン等により実現される。 (Input unit 350)
The input unit 350 can accept input of data and commands to the user terminal 300 . More specifically, the input unit 350 is implemented by a touch panel, keyboard, microphone, or the like.
　（出力部３６０）
　出力部３６０は、例えば、ディスプレイ、スピーカ、ランプ、映像出力端子、音声出力端子等により構成され、画像、点滅、音声等により各種の情報をユーザへ出力することができる。 (Output unit 360)
The output unit 360 includes, for example, a display, a speaker, a lamp, a video output terminal, an audio output terminal, etc., and can output various information to the user by means of images, blinking, audio, and the like.
　（通信部３７０）
　通信部３７０は、センサユニット２００やサーバ４００等の外部装置との間で情報の送受信を行うことができ、例えば、センサユニット２００からのセンシングデータをサーバ４００へ送信することができる。言い換えると、通信部３７０は、データの送受信を行う機能を有する通信インタフェースであり、具体的には、通信アンテナ、送受信回路やポート等の通信デバイスにより実現される。 (Communication unit 370)
The communication unit 370 can transmit and receive information to and from external devices such as the sensor unit 200 and the server 400 , and can transmit sensing data from the sensor unit 200 to the server 400 , for example. In other words, the communication unit 370 is a communication interface having a function of transmitting and receiving data, and is specifically realized by a communication device such as a communication antenna, a transmission/reception circuit, and a port.
　（記憶部３８０）
　記憶部３８０は、上述した制御部３３０が各種処理を実行するためのプログラム、情報等や、処理によって得た情報を格納することができる。記憶部３８０は、例えば、ハードディスク（Ｈａｒｄ　Ｄｉｓｋ：ＨＤ）などの磁気記録媒体や、フラッシュメモリ（ｆｌａｓｈ　ｍｅｍｏｒｙ）などの不揮発性メモリ（ｎｏｎｖｏｌａｔｉｌｅ　ｍｅｍｏｒｙ）等により実現される。 (storage unit 380)
The storage unit 380 can store programs, information, etc. for the above-described control unit 330 to execute various processes, and information obtained by the processes. The storage unit 380 is realized by, for example, a magnetic recording medium such as a hard disk (HD), a nonvolatile memory such as a flash memory, or the like.
　なお、本実施形態に係るユーザ端末３００は、図５に示される構成例に限定されるものではなく、例えば、他の機能部をさらに含んでいてもよい。 Note that the user terminal 300 according to the present embodiment is not limited to the configuration example shown in FIG. 5, and may further include other functional units, for example.
　＜２．５　サーバ＞
　次に、図６を参照して、本実施形態に係るサーバ４００の構成について説明する。図６は、本実施形態に係るサーバ４００の機能構成例を示すブロック図である。詳細には、サーバ４００は、図６に示すように、処理部４３０と、入力部４５０と、出力部４６０と、通信部４７０と、記憶部４８０とを主に有する。以下に、サーバ４００の有する各機能部について説明する。 <2.5 Server>
Next, the configuration of the server 400 according to this embodiment will be described with reference to FIG. FIG. 6 is a block diagram showing a functional configuration example of the server 400 according to this embodiment. Specifically, the server 400 mainly includes a processing unit 430, an input unit 450, an output unit 460, a communication unit 470, and a storage unit 480, as shown in FIG. Each functional unit of the server 400 will be described below.
　（処理部４３０）
　処理部４３０は、サーバ４００の各ブロックを制御することができる。当該処理部４３０は、例えば、ＣＰＵ、ＲＯＭ、ＲＡＭ等のハードウェアにより実現される。また、処理部４３０は、センサユニット２００からのセンシングデータと、ユーザが把持した際の日用品１００に関する重さの情報とに基づいて、ユーザの疾患に関するスコア（判断指標）を推定することができる。加えて、処理部４３０は、推定されたユーザの疾患に関する判断指標に基づいて、ユーザの疾患の状態を評価して、評価結果を生成、出力することもできる。詳細には、処理部４３０は、上述したこれら機能を実現するために、取得部４３２、推定部４３４、評価部４３６、及び情報出力部（出力部）４３８として機能する。以下に、本実施形態に係る処理部４３０のこれら機能の詳細について説明する。 (Processing unit 430)
The processing unit 430 can control each block of the server 400 . The processing unit 430 is implemented by hardware such as a CPU, ROM, and RAM, for example. In addition, the processing unit 430 can estimate a score (determination index) regarding the user's disease based on sensing data from the sensor unit 200 and weight information regarding the daily necessities 100 when held by the user. In addition, the processing unit 430 can also evaluate the state of the user's disease based on the estimated judgment index regarding the user's disease, and generate and output an evaluation result. Specifically, the processing unit 430 functions as an acquisition unit 432, an estimation unit 434, an evaluation unit 436, and an information output unit (output unit) 438 in order to realize these functions described above. Details of these functions of the processing unit 430 according to the present embodiment will be described below.
　～取得部４３２～
　取得部４３２は、センサユニット２００からユーザ端末３００を介して送信されたセンシングデータ等を取得し、取得したセンシングデータ等を後述する推定部４３４へ出力することができる。具体的には、取得部４３２は、例えば、センサユニット２００のＩＭＵ２１０や重量センサ２２０からセンシングデータを取得したり、日用品１００やユーザ端末３００のそれぞれ紐づけられた識別情報やユーザを特定する識別情報等を取得したりすることができる。 ~ Acquisition unit 432 ~
The acquisition unit 432 can acquire sensing data and the like transmitted from the sensor unit 200 via the user terminal 300 and output the acquired sensing data and the like to the estimation unit 434 described later. Specifically, the acquisition unit 432 acquires, for example, sensing data from the IMU 210 and the weight sensor 220 of the sensor unit 200, and identification information associated with the daily necessities 100 and the user terminal 300, and identification information identifying the user. etc. can be obtained.
　～推定部４３４～
　推定部４３４は、センサユニット２００から送信されたセンシングデータと、ユーザが把持した際の日用品１００に関する重さの情報とに基づいて、所定のアルゴリズムを適用することにより、ユーザの疾患に関するスコア（判断指標）を推定することができる。さらに、推定部４３４は、推定したスコアを後述する評価部４３６や記憶部４８０に出力することができる。 ~ Estimation unit 434 ~
The estimating unit 434 applies a predetermined algorithm based on the sensing data transmitted from the sensor unit 200 and information on the weight of the daily necessities 100 when held by the user to obtain the user's disease score (judgment index) can be estimated. Furthermore, the estimation unit 434 can output the estimated score to the evaluation unit 436 and the storage unit 480, which will be described later.
　詳細には、推定部４３４は、センサユニット２００からセンシングデータとともに送信された日用品１００に紐づけられた識別情報に基づき、予め記憶部４８０に格納したデータベースから、上記識別情報に紐づけられた日用品１００自体の重量の情報を取得する。さらに、推定部４３４は、センサユニット２００から重量センサ２２０によるセンシングデータを取得し、日用品１００に内包された液体等の重量を推定する。そして、推定部４３４は、日用品１００自体の重量と推定された日用品１００内の液体等の重量とに基づいて、ユーザが把持した際の日用品１００の重量の総和（ユーザが把持した際の日用品１００の重さの情報）を推定する。なお、本実施形態においては、全体の重量が変化しない日用品１００である場合には、推定部４３４は、記憶部４８０に格納したデータベースから日用品１００自体の重量の情報を取得することで、ユーザが把持した際の日用品１００の重さの情報を得ることができる。 Specifically, based on the identification information linked to the daily necessities 100 transmitted together with the sensing data from the sensor unit 200, the estimating unit 434 retrieves the daily necessities linked to the identification information from a database stored in advance in the storage unit 480. Obtain information on the weight of the 100 itself. Furthermore, the estimation unit 434 acquires sensing data from the weight sensor 220 from the sensor unit 200 and estimates the weight of the liquid or the like included in the daily necessities 100 . Based on the weight of the daily necessities 100 themselves and the estimated weight of the liquid or the like in the daily necessities 100, the estimating unit 434 calculates the sum of the weights of the daily necessities 100 held by the user (the sum of the weights of the daily necessities 100 held by the user). weight information). In the present embodiment, when the daily necessities 100 do not change in overall weight, the estimating unit 434 acquires information on the weight of the daily necessities 100 themselves from the database stored in the storage unit 480 so that the user can Information about the weight of the daily necessities 100 when held can be obtained.
　次に、推定部４３４は、加速度データや角速度データを含むセンシングデータに基づいて、日用品１００の移動軌跡を推定する。具体的には、推定部４３４は、例えば、日用品１００の所定の点を基準点とするＸ軸、Ｙ軸、Ｚ軸における加速度及び角速度を積分することにより、３次元空間上での上記基準点の移動軌跡や姿勢変化を推定することができる。 Next, the estimation unit 434 estimates the movement trajectory of the daily necessities 100 based on sensing data including acceleration data and angular velocity data. Specifically, for example, the estimation unit 434 integrates the acceleration and angular velocity in the X-axis, Y-axis, and Z-axis with a predetermined point on the daily necessities 100 as a reference point, thereby obtaining the reference point on the three-dimensional space. It is possible to estimate the movement trajectory and posture change of
　さらに、推定部４３４は、上述したように推定した、ユーザが把持した際の日用品１００の重さの情報と、日用品１００の移動軌跡とに基づいて、予め格納された複数の検査項目の中から、ユーザが日用品１００を把持した際の動作に紐づけ可能な検査項目を抽出する。具体的には、推定部４３４は、例えば図１０に示すような、疾患の状態を検査するための検査項目テーブル４８２の中から、ユーザが日用品１００を把持した際の動作に類似する動作を持つ検査項目を抽出する。この際、推定部４３４は、予め取得したユーザの属性情報（例えば、性別、年齢、身長、体重等の情報）を参照してもよく、もしくは、生体情報センサ等の他のセンサからのセンシングデータを参照してもよい。そして、推定部４３４は、抽出した検査項目に紐づけられたスコア算出式（抽出した前記検査項目に関する情報）等に基づき、ユーザの疾患に関するスコア（判断指標）を推定する。 Furthermore, the estimating unit 434 selects the weight of the daily necessities 100 when held by the user, estimated as described above, and the movement trajectory of the daily necessities 100 from among a plurality of pre-stored inspection items. , to extract inspection items that can be linked to the user's action when holding the daily necessities 100 . Specifically, the estimating unit 434 has a motion similar to the motion when the user grips the daily necessities 100 from the inspection item table 482 for inspecting the state of disease as shown in FIG. 10, for example. Extract inspection items. At this time, the estimation unit 434 may refer to previously acquired user attribute information (for example, information such as sex, age, height, and weight), or sensing data from other sensors such as a biological information sensor. You may refer to Then, the estimation unit 434 estimates a score (determination index) related to the user's disease based on a score calculation formula (information related to the extracted inspection item) or the like linked to the extracted inspection item.
　なお、本実施形態においては、推定部４３４により、重量センサ２２０によるセンシングデータに基づいて、日用品１００が内包する液体等の重量を推定することに限定されるものではない。例えば、推定部４３４は、ＩＭＵ２１０からの加速度データや角速度データを含むセンシングデータ（例えば、これらのセンシングデータから得られた日用品１００の振動等）に対して、予め機械学習により作成したアルゴリズムを用いて解析を行うことにより、日用品１００に内包された液体等の重量を推定してもよい。 Note that in the present embodiment, the estimating unit 434 is not limited to estimating the weight of the liquid or the like included in the daily necessities 100 based on sensing data from the weight sensor 220 . For example, the estimating unit 434 uses an algorithm created in advance by machine learning for sensing data including acceleration data and angular velocity data from the IMU 210 (for example, vibration of the daily necessities 100 obtained from these sensing data). By performing the analysis, the weight of the liquid or the like contained in the daily necessities 100 may be estimated.
　～評価部４３６～
　評価部４３６は、推定部４３４により推定されたユーザの疾患に関するスコア（判断指標）に基づいて、ユーザの疾患の状態を評価して、評価結果を生成し、後述する情報出力部４３８や記憶部４８０へ出力することができる。具体的には、評価部４３６は、例えば図１２に示すような判定テーブル４８４の中から、スコアの推定の際に抽出した検査項目に紐づけられた評価条件を抽出する。さらに、評価部４３６は、抽出された評価条件に従って、ユーザの状態が正常か異常かを評価し、異常と判定した場合には、医療従事者用端末５００へアラームを通知するように、後述する情報出力部４３８へ指示を出力する。 ~Evaluation Department 436~
The evaluation unit 436 evaluates the user's disease state based on the user's disease score (determination index) estimated by the estimation unit 434, generates an evaluation result, and outputs the information output unit 438 and storage unit described later. 480. Specifically, the evaluation unit 436 extracts, for example, from the determination table 484 shown in FIG. 12, evaluation conditions linked to the inspection items extracted when estimating the score. Furthermore, the evaluation unit 436 evaluates whether the user's condition is normal or abnormal according to the extracted evaluation conditions, and if it is determined to be abnormal, an alarm is sent to the medical staff terminal 500, which will be described later. An instruction is output to the information output unit 438 .
　なお、評価部４３６における評価方法は、上述した方法に限定されるものではなく、本実施形態においては他の方法を用いてもよい。例えば、本実施形態においては、同一の検査項目に関するスコアを記録し、当該スコアの経時変化に基づいて、ユーザの状態が正常か異常かを評価してもよい。この場合、評価部４３６は、例えば、所定の期間内におけるスコアに所定の閾値以上の変化があった場合には、異常と評価する。 The evaluation method in the evaluation unit 436 is not limited to the method described above, and other methods may be used in this embodiment. For example, in the present embodiment, the score for the same test item may be recorded, and whether the user's condition is normal or abnormal may be evaluated based on the change in the score over time. In this case, for example, the evaluation unit 436 evaluates as abnormal when the score changes by a predetermined threshold value or more within a predetermined period.
　～情報出力部４３８～
　情報出力部４３８は、評価部４３６からの評価結果等を、後述する通信部４７０を制御して、医療従事者用端末５００やユーザ端末３００に送信することができる。 - Information output unit 438 -
The information output unit 438 can control the communication unit 470 to be described later to transmit the evaluation results and the like from the evaluation unit 436 to the medical staff terminal 500 and the user terminal 300 .
　（入力部４５０）
　入力部４５０は、サーバ４００へのデータ、コマンドの入力を受け付けることができる。より具体的には、当該入力部４５０は、タッチパネル、キーボード等により実現される。 (Input unit 450)
The input unit 450 can accept input of data and commands to the server 400 . More specifically, the input unit 450 is implemented by a touch panel, keyboard, or the like.
　（出力部４６０）
　出力部４６０は、例えば、ディスプレイ等により構成され、画像等により各種の情報を出力することができる。 (Output unit 460)
The output unit 460 is configured by, for example, a display or the like, and can output various kinds of information in the form of images or the like.
　（通信部４７０）
　通信部４７０は、ユーザ端末３００や医療従事者用端末５００等の外部装置との間で情報（例えば、アラーム）の送受信を行うことができる。言い換えると、通信部４７０は、データの送受信を行う機能を有する通信インタフェースと言え、具体的には、通信アンテナ、送受信回路やポート等の通信デバイスにより実現される。 (Communication unit 470)
The communication unit 470 can transmit and receive information (for example, alarms) to and from external devices such as the user terminal 300 and the terminal 500 for medical staff. In other words, the communication unit 470 can be said to be a communication interface having a function of transmitting and receiving data, and is specifically realized by a communication device such as a communication antenna, a transmission/reception circuit, and a port.
　（記憶部４８０）
　記憶部４８０は、上述した処理部４３０が各種処理を実行するためのプログラム、情報等や、処理によって得た情報を格納することができる。詳細には、記憶部４８０は、疾患の状態を検査するための複数の検査項目に関する情報（例えば、検査項目テーブル４８２）や、推定されたユーザの疾患に関するスコアに基づいて、ユーザの疾患の状態を評価する際に使用する情報（例えば、判定テーブル４８４）を格納する。また、記憶部４８０は、ユーザの属性情報、ユーザの家族等の情報、ユーザの使用する日用品１００の重さに関する情報や、推定されたスコア（判断指標）等をユーザデータ４８６として格納してもよい。さらに、記憶部４８０は、上述した推定部４３４で推定の際に使用するモデルやアルゴリズムである推定モデル４８８等を格納していてもよい。なお、記憶部４８０は、例えば、ハードディスク等の磁気記録媒体や、フラッシュメモリ等の不揮発性メモリ等により実現される。 (storage unit 480)
The storage unit 480 can store programs, information, and the like for the processing unit 430 to execute various types of processing, and information obtained by the processing. Specifically, the storage unit 480 stores the user's disease state based on information on a plurality of test items for testing the disease state (for example, a test item table 482) and an estimated score on the user's disease. Stores information (for example, the determination table 484) used when evaluating the . In addition, the storage unit 480 may store user attribute information, information on the user's family, information on the weight of the daily necessities 100 used by the user, an estimated score (determination index), etc., as user data 486. good. Furthermore, the storage unit 480 may store an estimation model 488 or the like, which is a model or algorithm used for estimation by the estimation unit 434 described above. Note that the storage unit 480 is implemented by, for example, a magnetic recording medium such as a hard disk, or a non-volatile memory such as a flash memory.
　なお、本実施形態に係るサーバ４００は、図６に示される構成例に限定されるものではなく、例えば、他の機能部をさらに含んでいてもよい。さらに、サーバ４００は、ネットワーク８００で互いに通信可能に接続された複数の情報処理装置から構成されてもよい。また、サーバ４００の機能の少なくとも一部は、上述したユーザ端末３００によって実行されてもよく、もしくは、サーバ４００は、ユーザ端末３００又は医療従事者用端末５００と一体となった装置として構成されてもよい。 Note that the server 400 according to the present embodiment is not limited to the configuration example shown in FIG. 6, and may further include other functional units, for example. Furthermore, the server 400 may be composed of a plurality of information processing devices communicably connected to each other via the network 800 . At least part of the functions of the server 400 may be executed by the user terminal 300 described above, or the server 400 may be configured as a device integrated with the user terminal 300 or the terminal 500 for medical staff. good too.
　＜２．６　医療従事者用端末＞
　次に、本実施形態に係る医療従事者用端末５００の構成の一例について、図７を参照して説明する。図７は、本実施形態に係る医療従事者用端末５００の機能構成例を示すブロック図である。本実施形態に係る医療従事者用端末５００は、図７に示すように、制御部５３０と、入力部５５０と、出力部５６０と、通信部５７０と、記憶部５８０とを主に有する。以下に、医療従事者用端末５００の有する各機能部について説明する。 <2.6 Terminal for medical staff>
Next, an example of the configuration of the medical staff terminal 500 according to this embodiment will be described with reference to FIG. FIG. 7 is a block diagram showing a functional configuration example of the medical staff terminal 500 according to this embodiment. The medical staff terminal 500 according to this embodiment mainly includes a control unit 530, an input unit 550, an output unit 560, a communication unit 570, and a storage unit 580, as shown in FIG. Each functional unit of the medical staff terminal 500 will be described below.
　（制御部５３０）
　制御部５３０は、医療従事者用端末５００の各ブロックを制御することができる。当該制御部５３０は、例えば、ＣＰＵ、ＲＯＭ、ＲＡＭ等のハードウェアにより実現される。 (control unit 530)
The control unit 530 can control each block of the medical staff terminal 500 . The control unit 530 is implemented by hardware such as CPU, ROM, and RAM, for example.
　（入力部５５０）
　入力部５５０は、医療従事者用端末５００へのデータ、コマンドの入力を受け付けることができる。より具体的には、当該入力部５５０は、タッチパネル、キーボード、マイクロフォン等により実現される。 (Input unit 550)
The input unit 550 can accept input of data and commands to the medical staff terminal 500 . More specifically, the input unit 550 is implemented by a touch panel, keyboard, microphone, or the like.
　（出力部５６０）
　出力部５６０は、例えば、ディスプレイ、スピーカ、ランプ、映像出力端子、音声出力端子等により構成され、画像、点滅、音声等により各種の情報を医療従事者へ出力することができる。 (Output unit 560)
The output unit 560 is composed of, for example, a display, a speaker, a lamp, a video output terminal, an audio output terminal, etc., and can output various information to the medical staff by means of images, flashes, sounds, and the like.
　（通信部５７０）
　通信部５７０は、サーバ４００等の外部装置との間で情報（例えば、アラーム）の送受信を行うことができる。言い換えると、通信部５７０は、データの送受信を行う機能を有する通信インタフェースと言え、具体的には、通信アンテナ、送受信回路やポート等の通信デバイスにより実現される。 (Communication unit 570)
The communication unit 570 can transmit and receive information (for example, alarms) to and from an external device such as the server 400 . In other words, the communication unit 570 can be said to be a communication interface having a function of transmitting and receiving data, and is specifically realized by a communication device such as a communication antenna, a transmission/reception circuit, and a port.
　（記憶部５８０）
　記憶部５８０は、上述した制御部５３０が各種処理を実行するためのプログラム、情報等や、処理によって得た情報を格納することができる。なお、記憶部５８０は、例えば、ハードディスクなどの磁気記録媒体や、フラッシュメモリ等の不揮発性メモリ等により実現される。 (storage unit 580)
The storage unit 580 can store programs, information, etc. for the above-described control unit 530 to execute various processes, and information obtained by the processes. Note that the storage unit 580 is realized by, for example, a magnetic recording medium such as a hard disk, a non-volatile memory such as a flash memory, or the like.
　なお、本実施形態に係る医療従事者用端末５００は、図７に示される構成例に限定されるものではなく、例えば、他の機能部をさらに含んでいてもよい。 Note that the medical staff terminal 500 according to the present embodiment is not limited to the configuration example shown in FIG. 7, and may further include other functional units, for example.
　＜２．７　情報処理方法＞
　（システム全体）
　以上、本開示の実施形態に係る情報処理システム１０及び当該情報処理システム１０に含まれる各装置の詳細について説明した。次に、本実施形態に係る情報処理方法について、図８を参照して説明する。図８は、本実施形態に係る情報処理方法のシーケンス図である。図８に示すように、本実施形態に係る情報処理方法には、ステップＳ１００からステップＳ５００までの複数のステップが含まれている。以下に、本実施形態に係る情報処理方法に含まれる各ステップの詳細を説明する。 <2.7 Information processing method>
(whole system)
The details of the information processing system 10 and each device included in the information processing system 10 according to the embodiment of the present disclosure have been described above. Next, an information processing method according to this embodiment will be described with reference to FIG. FIG. 8 is a sequence diagram of the information processing method according to this embodiment. As shown in FIG. 8, the information processing method according to this embodiment includes a plurality of steps from step S100 to step S500. Details of each step included in the information processing method according to the present embodiment will be described below.
　センサユニット２００は、ＩＭＵ２１０や重量センサ２２０からのセンシングデータとともに、自身が搭載された日用品１００を識別する識別情報や自身を識別する識別情報をユーザ端末３００へ送信する（ステップＳ１００）。 The sensor unit 200 transmits, to the user terminal 300, the sensing data from the IMU 210 and the weight sensor 220 as well as the identification information identifying the daily necessities 100 on which it is mounted and the identification information identifying itself (step S100).
　次に、ユーザ端末３００は、上述したステップＳ１００においてセンサユニット２００から取得したセンシングデータ及び識別情報とともに、自身を識別する識別情報もしくはユーザを識別する識別情報をサーバ４００へ送信する（ステップＳ２００）。 Next, the user terminal 300 transmits identification information for identifying itself or identification information for identifying the user to the server 400 together with the sensing data and identification information acquired from the sensor unit 200 in step S100 described above (step S200).
　そして、サーバ４００は、上述したステップＳ２００においてユーザ端末３００から取得したセンシングデータや識別情報に基づき、ユーザの疾患に関するスコアを推定する（ステップＳ３００）。なお、当該ステップＳ３００の詳細については後述する。 Then, the server 400 estimates the score regarding the user's disease based on the sensing data and identification information acquired from the user terminal 300 in step S200 described above (step S300). Details of the step S300 will be described later.
　さらに、サーバ４００は、上述したステップＳ３００で推定したスコアに基づき、ユーザの疾患の状態を評価し、評価結果に応じて医療従事者用端末５００やユーザ端末３００にアラームを送信する（ステップＳ４００）。なお、当該ステップＳ４００の詳細については後述する。 Furthermore, the server 400 evaluates the user's disease state based on the score estimated in step S300 described above, and transmits an alarm to the medical staff terminal 500 or the user terminal 300 according to the evaluation result (step S400). . Details of the step S400 will be described later.
　そして、医療従事者用端末５００及びユーザ端末３００は、受信したアラームに基づき、医療従事者、ユーザや家族に向けてアラームを出力する（ステップＳ５００）。例えば、アラームの内容としては、ユーザに精密検査を促すような情報や、治療を促すような情報を挙げることができる。 Then, the medical staff terminal 500 and the user terminal 300 output an alarm to the medical staff, the user, and the family based on the received alarm (step S500). For example, the content of the alarm may include information prompting the user to perform a detailed examination or information prompting treatment.
　（推定方法）
　次に、本実施形態に係る情報処理方法のステップＳ３００の推定方法について、図９及び図１０を参照して説明する。図９は、本実施形態に係る情報処理方法のフローチャートであり、図１０は、本実施形態に係る検査項目テーブル４８２の一例を示す図である。図９に示すように、本実施形態に係る推定方法には、ステップＳ３０１からステップＳ３０５までの複数のステップが含まれている。以下に、本実施形態に係る推定方法に含まれる各ステップの詳細を説明する。 (estimation method)
Next, the estimation method in step S300 of the information processing method according to this embodiment will be described with reference to FIGS. 9 and 10. FIG. FIG. 9 is a flowchart of the information processing method according to this embodiment, and FIG. 10 is a diagram showing an example of the inspection item table 482 according to this embodiment. As shown in FIG. 9, the estimation method according to this embodiment includes a plurality of steps from step S301 to step S305. Details of each step included in the estimation method according to the present embodiment will be described below.
　サーバ４００は、ユーザが把持した際の日用品１００の重さを推定する（ステップＳ３０１）。例えば、サーバ４００は、センサユニット２００からセンシングデータとともに送信された日用品１００に紐づけられた識別情報に基づき、予め記憶部４８０に格納したデータベースから、上記識別情報に紐づけられた日用品１００自体の重量の情報を取得する。さらに、サーバ４００は、センサユニット２００から重量センサ２２０によるセンシングデータを取得し、日用品１００に内包された液体等の重量を推定する。そして、推定部４３４は、日用品１００自体の重量と推定された日用品１００内の液体等の重量とに基づいて、ユーザが把持した際の日用品１００の重量の総和（ユーザが把持した際の日用品１００の重さの情報）を推定する。 The server 400 estimates the weight of the daily necessities 100 held by the user (step S301). For example, based on the identification information linked to the daily necessities 100 transmitted together with the sensing data from the sensor unit 200, the server 400 retrieves the daily necessities 100 linked to the identification information from a database stored in advance in the storage unit 480. Get weight information. Furthermore, the server 400 acquires sensing data from the weight sensor 220 from the sensor unit 200 and estimates the weight of the liquid or the like included in the daily necessities 100 . Based on the weight of the daily necessities 100 themselves and the estimated weight of the liquid or the like in the daily necessities 100, the estimating unit 434 calculates the sum of the weights of the daily necessities 100 held by the user (the sum of the weights of the daily necessities 100 held by the user). weight information).
　次に、サーバ４００は、ＩＭＵ２１０からのセンシングデータに変化があったことに基づいて、ユーザが日用品１００を把持したことを検出する（ステップＳ３０２）。 Next, server 400 detects that the user has held daily necessities 100 based on the change in sensing data from IMU 210 (step S302).
　そして、サーバ４００は、加速度データや角速度データ等を含むセンシングデータに基づいて、ユーザの動作によって移動した日用品１００の移動軌跡を推定する（ステップＳ３０３）。例えば、サーバ４００は、日用品１００の所定の点を基準点とするＸ軸、Ｙ軸、Ｚ軸における加速度及び角速度を積分することにより、３次元空間上での上記基準点の移動軌跡や姿勢変化を推定することができる。 Then, the server 400 estimates the movement trajectory of the daily necessities 100 moved by the user's action based on sensing data including acceleration data, angular velocity data, etc. (step S303). For example, the server 400 integrates the acceleration and angular velocity on the X-axis, Y-axis, and Z-axis with a predetermined point of the daily article 100 as a reference point, and calculates the movement trajectory and attitude change of the reference point in the three-dimensional space. can be estimated.
　次に、サーバ４００は、ユーザが把持した際の日用品１００の重さの情報と日用品１００の移動軌跡とに基づいて、例えば図１０に示すような検査項目テーブル４８２の中から、ユーザが日用品１００を把持した際の動作に類似する動作を持つ検査項目を抽出する（ステップＳ３０４）。サーバ４００は、例えば、ユーザが把持した際の日用品１００の重さに近い重さ情報を持ち、且つ、日用品１００の移動軌跡に類似する軌跡情報を持つ検査項目を抽出する。なお、上記検査項目テーブル４８２には、例えば、図１Ａ及び図１Ｂで示すような各計測項目に紐づけられる軌跡情報や重さ情報等が格納されている。 Next, server 400 selects daily necessities 100 from inspection item table 482 as shown in FIG. is extracted (step S304). The server 400 , for example, extracts inspection items having weight information close to the weight of the daily necessities 100 held by the user and track information similar to the moving track of the daily necessities 100 . Note that the inspection item table 482 stores, for example, trajectory information, weight information, and the like associated with each measurement item as shown in FIGS. 1A and 1B.
　そして、サーバ４００は、上述したステップＳ３０４において抽出した検査項目に紐づけられたスコア算出アルゴリズムに定められた算出式（図１０　参照）に基づき、ユーザの疾患に関するスコア（判断指標）を推定し、登録する（ステップＳ３０５）。 Then, the server 400 estimates the score (determination index) related to the user's disease based on the calculation formula (see FIG. 10) defined in the score calculation algorithm linked to the inspection item extracted in step S304 described above, Register (step S305).
　（評価方法）
　次に、本実施形態に係る情報処理方法のステップＳ４００の評価方法について、図１１及び図１２を参照して説明する。図１１は、本実施形態に係る情報処理方法のフローチャートであり、図１２は、本実施形態に係る判定テーブル４８４の一例を示す図である。図１１に示すように、本実施形態に係る評価方法には、ステップＳ４０１からステップＳ４０３までの複数のステップが含まれている。以下に、本実施形態に係る推定方法に含まれる各ステップの詳細を説明する。 (Evaluation method)
Next, the evaluation method of step S400 of the information processing method according to this embodiment will be described with reference to FIGS. 11 and 12. FIG. FIG. 11 is a flowchart of the information processing method according to this embodiment, and FIG. 12 is a diagram showing an example of the determination table 484 according to this embodiment. As shown in FIG. 11, the evaluation method according to this embodiment includes a plurality of steps from step S401 to step S403. Details of each step included in the estimation method according to the present embodiment will be described below.
　まずは、サーバ４００は、上述したステップＳ３００の一連の処理によって得られたユーザの疾患に関するスコア（判断指標）を取得する（ステップＳ４０１）。 First, the server 400 acquires the score (determination index) regarding the user's disease obtained by the series of processes in step S300 described above (step S401).
　次に、サーバ４００は、例えば、図１２に示すような判定テーブル４８４の中から、スコアの推定の際に抽出した検査項目に紐づけられた評価条件を抽出し、抽出された評価条件に従って、ユーザに精密検査や治療が必要かを評価する（ステップＳ４０２）。具体的には、サーバ４００は、判定テーブル４８４に定められたルール（例えば、スコアを所定の閾値と比較する）に従って、取得したスコアに基づき、ユーザの疾患の状態を評価する。サーバ４００は、ユーザの状態が異常と評価した場合には、ユーザに精密検査や治療が必要と判断し（ステップＳ４０２：Ｙｅｓ）、後述するステップＳ４０３の処理へ進む。一方、サーバ４００は、ユーザの疾患の状態が正常と評価した場合には、ユーザに精密検査や治療が必要ないと判定し（ステップＳ４０２：Ｎｏ）、当該処理を終了する。 Next, the server 400 extracts, for example, evaluation conditions linked to the inspection items extracted during score estimation from the determination table 484 shown in FIG. 12, and according to the extracted evaluation conditions, Evaluate whether the user needs a detailed examination or treatment (step S402). Specifically, the server 400 evaluates the disease state of the user based on the score obtained according to the rules defined in the determination table 484 (for example, comparing the score with a predetermined threshold). When the server 400 evaluates that the user's condition is abnormal, the server 400 determines that the user needs a detailed examination or treatment (step S402: Yes), and proceeds to the process of step S403 described later. On the other hand, when the user's disease state is evaluated as normal, the server 400 determines that the user does not need a detailed examination or treatment (step S402: No), and ends the process.
　さらに、サーバ４００は、医療従事者、ユーザやユーザの家族に向けて、精密検査を促すような情報や、治療を促すような情報が含まれるアラームを通知する（ステップＳ４０３）。 Furthermore, the server 400 notifies the medical staff, the user, and the user's family of an alarm that includes information that prompts detailed examination and information that prompts treatment (step S403).
　以上のように、本実施形態によれば、ユーザに負担をかけることなく、日常生活の中でユーザの動作を計測し、ユーザの疾患の状態をスコア化することができる。さらに、本実施形態においては、日常的に取得された上記スコアに基づき、医療従事者やユーザ等へ早急にアラームを通知することができる。その結果、本実施形態によれば、早期発見、早期治療の機会をユーザに提供することが可能となる。 As described above, according to the present embodiment, it is possible to measure the user's actions in daily life and score the user's disease state without imposing a burden on the user. Furthermore, in the present embodiment, it is possible to quickly issue an alarm to medical staff, users, etc., based on the score obtained on a daily basis. As a result, according to this embodiment, it is possible to provide the user with an opportunity for early detection and early treatment.
　＜＜３．　本開示の第２の実施形態＞＞
　＜３．１　概要＞
　上述した本開示の第１の実施形態においては、ユーザの疾患に関するスコア（判断指標）を推定する際には、推定したユーザが把持した際の日用品１００の重さに関する情報と日用品１００の移動軌跡とに基づいて、検査項目テーブル４８２の中から、ユーザが日用品１００を把持した際の動作に紐づけ可能な検査項目を抽出していた。しかしながら、本開示においては、このような推定方法に限定されるものではなく、例えば、推定された日用品１００の移動軌跡の中から、所定の動作を検出して上記スコアを推定してもよい。このようにすることで、本実施形態によれば、検査項目テーブル４８２の中にユーザの動作に類似する検査項目が見つからない場合に探索を続けるといった処理を行うことを避けることができ、処理によって生じる負荷の増加を抑えることができる。以下、このような推定を行う本開示の第２の実施形態を説明する。 <<3. Second embodiment of the present disclosure>>
<3.1 Overview>
In the above-described first embodiment of the present disclosure, when estimating a score (determination index) related to a user's disease, information about the estimated weight of the daily necessities 100 held by the user and the movement trajectory of the daily necessities 100 , the inspection item that can be linked to the action when the user grips the daily necessities 100 is extracted from the inspection item table 482 . However, the present disclosure is not limited to such an estimation method, and for example, the score may be estimated by detecting a predetermined motion from the estimated movement trajectory of the daily necessities 100 . By doing so, according to the present embodiment, it is possible to avoid performing the process of continuing the search when an inspection item similar to the user's motion is not found in the inspection item table 482. The resulting increase in load can be suppressed. A second embodiment of the present disclosure that performs such estimation will be described below.
　なお、本実施形態においては、情報処理システム１０の構成及び当該情報処理システム１０に含まれる各装置の構成については、以下の点を除いて、上述した第１の実施形態と同様であるため、ここではこれらの説明を省略する。本実施形態においては、サーバ４００の推定部４３４は、推定された日用品１００の移動軌跡の中から、特定の動作（例えば、直上挙上等）に対応する移動軌跡を検出し、検出された移動軌跡と、ユーザが把持した際の日用品１００に関する重さの情報とに基づいて、ユーザの疾患に関するスコア（判断指標）を推定する。 In this embodiment, the configuration of the information processing system 10 and the configuration of each device included in the information processing system 10 are the same as in the above-described first embodiment except for the following points. Descriptions of these are omitted here. In the present embodiment, the estimating unit 434 of the server 400 detects a movement trajectory corresponding to a specific action (e.g., straight up) from the estimated movement trajectory of the daily necessities 100, and Based on the trajectory and information on the weight of the daily necessities 100 when held by the user, the user's disease score (determination index) is estimated.
　＜３．２　情報処理方法＞
　次に、本実施形態に係る情報処理方法について説明する。本実施形態における情報処理の全体の流れや評価方法は、図８及び図９を参照して説明した第１の実施形態と同様であるため、ここではこれらの説明を省略する。さらに、本実施形態に係る推定方法については、第１の実施形態と異なるため、図１３を参照して本実施形態に係る推定方法を説明する。図１３は、本実施形態に係る情報処理方法のフローチャートである。 <3.2 Information processing method>
Next, an information processing method according to this embodiment will be described. Since the overall flow of information processing and the evaluation method in this embodiment are the same as those in the first embodiment described with reference to FIGS. 8 and 9, description thereof will be omitted here. Furthermore, since the estimation method according to this embodiment differs from that of the first embodiment, the estimation method according to this embodiment will be described with reference to FIG. FIG. 13 is a flowchart of an information processing method according to this embodiment.
　詳細には、図１３に示すように、本実施形態に係る推定方法には、ステップＳ３１１からステップＳ３１４までの複数のステップが含まれている。以下に、本実施形態に係る推定方法に含まれる各ステップの詳細を説明する。 Specifically, as shown in FIG. 13, the estimation method according to this embodiment includes a plurality of steps from step S311 to step S314. Details of each step included in the estimation method according to the present embodiment will be described below.
　サーバ４００は、ユーザが把持した際の日用品１００の重さを推定する（ステップＳ３１１）。詳細については、図９を参照して説明した第１の実施形態の情報処理方法のステップＳ３０１と同様であるため、ここでは詳細な説明を省略する。 The server 400 estimates the weight of the daily necessities 100 held by the user (step S311). The details are the same as step S301 of the information processing method of the first embodiment described with reference to FIG. 9, so detailed description is omitted here.
　次に、サーバ４００は、加速度データや角速度データを含むセンシングデータに基づいて、ユーザの動作によって移動する日用品１００の移動軌跡を推定する（ステップＳ３１２）。詳細については、図９を参照して説明した第１の実施形態の情報処理方法のステップＳ３０３と同様であるため、ここでは詳細な説明を省略する。 Next, the server 400 estimates the movement trajectory of the daily necessities 100 moved by the user's actions based on sensing data including acceleration data and angular velocity data (step S312). The details are the same as step S303 of the information processing method of the first embodiment described with reference to FIG. 9, so detailed description is omitted here.
　そして、サーバ４００は、推定された日用品１００の移動軌跡の中から、例えば、記憶部４８０に格納されたデータベース内の特定の動作（所定の動作）に対応する移動軌跡の情報を参照して、該当する移動軌跡を検出する（ステップＳ３１３）。なお、上記データベースにおいては、図１Ａ及び図１Ｂで示すような動作が特定の動作として規定されているものとする。 Then, the server 400 refers to, for example, movement trajectory information corresponding to a specific action (predetermined action) in the database stored in the storage unit 480 from the estimated movement trajectory of the daily necessities 100, A corresponding movement locus is detected (step S313). It should be noted that, in the above database, operations as shown in FIGS. 1A and 1B are defined as specific operations.
　そして、サーバ４００は、上述したステップＳ３１３において抽出した特定の動作に紐づけられたスコア算出式等に基づき、ユーザの疾患に関するスコア（判断指標）を推定する（ステップＳ３１４）。 Then, the server 400 estimates the score (determination index) related to the user's disease based on the score calculation formula or the like linked to the specific action extracted in step S313 described above (step S314).
　以上のように、本実施形態によれば、ユーザに負担をかけることなく、日常生活の中でユーザの動作を計測し、ユーザの疾患の状態をスコア化することができる。さらに、本実施形態によれば、特定の動作に対応する移動軌跡を検出した際にのみ、スコアの算出を行うため、処理によって生じる負荷の増加を抑えることができる。 As described above, according to the present embodiment, it is possible to measure the user's actions in daily life and score the user's disease state without imposing a burden on the user. Furthermore, according to the present embodiment, the score is calculated only when a movement trajectory corresponding to a specific action is detected, so an increase in load caused by processing can be suppressed.
　＜＜４．　本開示の第３の実施形態＞＞
　＜４．１　概要＞
　本開示においては、第１及び第２の実施形態に係る推定方法に限定されるものではなく、例えば、予め機械学習により作成したアルゴリズムを用いて、ユーザの疾患に関するスコア（判断指標）を推定してもよい。この場合、アルゴリズムを生成するために多くのセンシングデータを準備する必要があるが、アルゴリズムの質によるが、ユーザが、検査項目等で定められた動作に類似しない動作を行った場合であっても、精度よくユーザの疾患の状態をスコア化することができる。以下、このような推定を行う本開示の第３の実施形態を説明する。 <<4. Third embodiment of the present disclosure >>
<4.1 Overview>
The present disclosure is not limited to the estimation methods according to the first and second embodiments. For example, an algorithm created in advance by machine learning is used to estimate a score (determination index) related to a user's disease. may In this case, it is necessary to prepare a large amount of sensing data in order to generate the algorithm. , the user's disease state can be scored with high accuracy. A third embodiment of the present disclosure that performs such estimation will be described below.
　なお、本実施形態においては、情報処理システム１０の構成及び当該情報処理システム１０に含まれる各装置の構成について、以下の点を除いて、上述した第１の実施形態と同様であるため、ここではこれらの説明を省略する。本実施形態においては、サーバ４００の推定部４３４は、予め機械学習により作成したアルゴリズムを用いて、ユーザが把持した際の日用品１００に関する重さの情報、及び、日用品１００の移動軌跡を示すセンシングデータに基づいて、ユーザの疾患に関するスコア（判断指標）を推定する。 In this embodiment, the configuration of the information processing system 10 and the configuration of each device included in the information processing system 10 are the same as those of the above-described first embodiment except for the following points. We will omit these descriptions. In this embodiment, the estimating unit 434 of the server 400 uses an algorithm created in advance by machine learning to obtain information on the weight of the daily necessities 100 when held by the user and sensing data indicating the movement trajectory of the daily necessities 100. Based on, the score (determination index) regarding the user's disease is estimated.
　＜４．２　情報処理方法＞
　次に、本実施形態に係る情報処理方法について説明するが、本実施形態における情報処理の全体の流れや評価方法は、図８及び図９を参照して説明した第１の実施形態と同様であるため、ここではその説明を省略する。また、本実施形態に係る推定方法については、第１の実施形態と異なるため、図１４を参照して、本実施形態に係る推定方法を説明する。図１４は、本実施形態に係る情報処理方法のフローチャートである。 <4.2 Information processing method>
Next, an information processing method according to this embodiment will be described. The overall flow of information processing and evaluation method in this embodiment are the same as those in the first embodiment described with reference to FIGS. Therefore, its description is omitted here. Also, since the estimation method according to this embodiment is different from that of the first embodiment, the estimation method according to this embodiment will be described with reference to FIG. 14 . FIG. 14 is a flowchart of an information processing method according to this embodiment.
　詳細には、図１４に示すように、本実施形態に係る推定方法には、ステップＳ３２１からステップＳ３２３までの複数のステップが含まれている。以下に、本実施形態に係る推定方法に含まれる各ステップの詳細を説明する。 Specifically, as shown in FIG. 14, the estimation method according to this embodiment includes a plurality of steps from step S321 to step S323. Details of each step included in the estimation method according to the present embodiment will be described below.
　サーバ４００は、ユーザが把持した際の日用品１００の重さを推定する（ステップＳ３２１）。詳細については、図９を参照して説明した第１の実施形態の情報処理方法のステップＳ３０１と同様であるため、ここでは詳細な説明を省略する。 The server 400 estimates the weight of the daily necessities 100 held by the user (step S321). The details are the same as step S301 of the information processing method of the first embodiment described with reference to FIG. 9, so detailed description is omitted here.
　次に、サーバ４００は、加速度データや角速度データを含むセンシングデータに基づいて、ユーザの動作によって移動する日用品１００の移動軌跡を推定する（ステップＳ３２２）。詳細については、図９を参照して説明した第１の実施形態の情報処理方法のステップＳ３０３と同様であるため、ここでは詳細な説明を省略する。 Next, the server 400 estimates the movement trajectory of the daily necessities 100 moved by the user's actions, based on sensing data including acceleration data and angular velocity data (step S322). The details are the same as step S303 of the information processing method of the first embodiment described with reference to FIG. 9, so detailed description is omitted here.
　さらに、サーバ４００は、予め機械学習により作成したアルゴリズムを用いて、ユーザが把持した際の日用品１００に関する重さの情報、及び、日用品１００の移動軌跡の情報に基づいて、直接的にユーザの疾患に関するスコア（判断指標）を推定する（ステップＳ３２３）。なお、本実施形態においては、サーバ４００は、日用品１００の移動軌跡を推定する代わりに、ユーザが把持した際の日用品１００に関する重さの情報、及び、加速度データや角速度データを含むセンシングデータに基づいて、直接的にユーザの疾患に関するスコアを推定してもよい。 Furthermore, the server 400 uses an algorithm created in advance by machine learning to directly detect the disease of the user based on information on the weight of the daily necessities 100 when held by the user and information on the movement trajectory of the daily necessities 100 . is estimated (step S323). In the present embodiment, instead of estimating the movement trajectory of the daily necessities 100, the server 400 is based on sensing data including information on the weight of the daily necessities 100 held by the user and acceleration data and angular velocity data. may be used to directly estimate the user's disease score.
　以下に、上記アルゴリズムの生成方法の一例について、図１５を参照して説明する。図１５は、本実施形態を説明するための説明図である。 An example of the algorithm generation method will be described below with reference to FIG. FIG. 15 is an explanatory diagram for explaining this embodiment.
　例えば、サーバ４００、もしくは、図示しない他の情報処理装置に、ユーザが把持した際の日用品１００に関する重さ情報、及び、日用品１００の移動軌跡の情報等を入力し、サーバ４００の処理部４３０等が有する学習器４４０に機械学習を行わせる。詳細には、図１５に示すように、サーバ４００又は他の情報処理装置には、サポートベクターレグレッションやディープニューラルネットワーク等の教師付き学習器４４０が備わっているものとする。学習器４４０に、センサユニット２００から取得されたセンシングデータに基づく情報（ユーザが把持した際の日用品１００に関する重さ情報、及び、日用品１００の移動軌跡の情報）と、当該ユーザに対して専門医が診断した結果であるユーザの疾患に関するスコア（判断指標）とを入力信号及び教師信号として入力する。当該学習器４４４０は、所定の規則に従ってこれら情報の間の関係について機械学習を行う。 For example, the server 400 or another information processing device (not shown) receives information on the weight of the daily necessities 100 held by the user, information on the movement trajectory of the daily necessities 100, etc. performs machine learning on the learner 440 of Specifically, as shown in FIG. 15, the server 400 or other information processing device is assumed to have a supervised learner 440 such as support vector regression or deep neural network. Learning device 440 stores information based on sensing data acquired from sensor unit 200 (weight information on daily necessities 100 when held by a user and information on the movement trajectory of daily necessities 100), and a specialist doctor for the user. A score (judgment index) relating to the user's disease, which is the result of diagnosis, is input as an input signal and a teacher signal. The learner 4440 performs machine learning on the relationship between these pieces of information according to predetermined rules.
　このように、当該学習器４４０は、多数の教師信号及び入力信号の対が入力され、これら入力に対して機械学習を行うことにより、ユーザが把持した際の日用品１００に関する重さ情報、及び、日用品１００の移動軌跡の情報と、ユーザの疾患に関するスコアとの関係を示す関係情報を示す推定モデル４８８を上記アルゴリズムとして構築することができる。また、学習器４４０には、日用品１００の移動軌跡の情報の代わりに、加速度データや角速度データを含むセンシングデータが入力されてもよい。さらに、学習器４４０には、ユーザの属性情報等が入力されてもよく、このような情報は、入力対象をグルーピングする際の情報や、入力対象を解析するための情報として使用することができる。また、本実施形態においては、学習器４４０は、半教師付き学習器や弱教師付き学習器であってもよい。 In this way, the learning device 440 receives a large number of pairs of teacher signals and input signals, and performs machine learning on these inputs to obtain weight information about the daily necessities 100 when held by the user, and An estimation model 488 indicating relationship information indicating the relationship between the information on the movement trajectory of the daily necessities 100 and the user's disease score can be constructed as the above algorithm. Further, sensing data including acceleration data and angular velocity data may be input to the learning device 440 instead of the information on the movement trajectory of the daily necessities 100 . Furthermore, user attribute information and the like may be input to the learning device 440, and such information can be used as information for grouping input targets and information for analyzing input targets. . Also, in this embodiment, the learner 440 may be a semi-supervised learner or a weakly supervised learner.
　以上のように、本実施形態によれば、ユーザに負担をかけることなく、日常生活の中でユーザの動作を計測し、ユーザの疾患の状態をスコア化することができる。さらに、本実施形態によれば、ユーザが、検査項目等で定められた動作に類似しない動作を行った場合であっても、精度よくユーザの疾患の状態をスコア化することができる。 As described above, according to the present embodiment, it is possible to measure the user's actions in daily life and score the user's disease state without imposing a burden on the user. Furthermore, according to the present embodiment, even if the user performs an action that is not similar to the action defined by the examination item or the like, the user's disease state can be scored with high accuracy.
　＜＜５．　変形例＞＞
　＜５．１　変形例１＞
　これまで説明した本開示の実施形態においては、日用品１００に搭載されたセンサユニット２００からのセンシングデータに基づいて、日用品１００の移動軌跡を推定していたが、本開示はそれに限定されるものではない。そこで、本開示の実施形態の変形例１を、図１６を参照して説明する。図１６は、実施形態の変形例に係る情報処理システム１０ａの概略的な機能構成を示したシステム図である。 <<5. Modification>>
<5.1 Modification 1>
In the embodiments of the present disclosure described so far, the movement trajectory of the daily necessities 100 was estimated based on the sensing data from the sensor unit 200 mounted on the daily necessities 100, but the present disclosure is not limited thereto. do not have. Therefore, Modification 1 of the embodiment of the present disclosure will be described with reference to FIG. 16 . FIG. 16 is a system diagram showing a schematic functional configuration of an information processing system 10a according to a modification of the embodiment.
　図１６に示すように、本変形例に係る情報処理システム１０ａは、第１の実施形態と同様に、日用品１００に具設されたセンサユニット２００ａと、ユーザ端末３００と、サーバ４００と、医療従事者用端末５００とを含み、さらに、カメラ２９０を含む。 As shown in FIG. 16, an information processing system 10a according to this modified example includes a sensor unit 200a provided in a daily necessities 100, a user terminal 300, a server 400, and a medical staff, as in the first embodiment. and a user terminal 500, and further includes a camera 290.
　詳細には、本変形例においては、第１の実施形態に係るセンサユニット２００のＩＭＵ２１０の代わりに、カメラ２９０を用いて日用品１００を撮像することにより、日用品１００の移動軌跡を取得する。具体的には、ユーザの生活する部屋（例えば病室等）の壁に複数のカメラ２９０を設置する。そして、これらカメラ２９０は、ユーザの使用する日用品１００を撮像する。サーバ４００は、これらカメラ２９０の位置情報と、各カメラ２９０で捉えた日用品１００の動画像とを解析することにより、ユーザによって担持され移動する日用品１００の移動軌跡を得ることができる。なお、本変形例においては、解析を容易にするために、日用品１００の外表面に特徴的な形状や色彩を持つマーカ１０２が取り付けられていることが好ましい。さらに、本変形例においては、日用品１００の重さについては、第１の実施形態と同様に、センサユニット２００の重量センサ２２０によるセンシングデータに基づいて推定することができる。 Specifically, in this modified example, instead of the IMU 210 of the sensor unit 200 according to the first embodiment, the camera 290 is used to capture images of the daily necessities 100 to acquire the movement locus of the daily necessities 100 . Specifically, a plurality of cameras 290 are installed on the wall of the room where the user lives (for example, a hospital room). These cameras 290 capture images of the daily necessities 100 used by the user. Server 400 analyzes the position information of these cameras 290 and the moving images of daily necessities 100 captured by each camera 290 to obtain the movement trajectory of daily necessities 100 carried and moved by the user. In addition, in this modification, it is preferable that a marker 102 having a characteristic shape and color is attached to the outer surface of the daily necessities 100 in order to facilitate the analysis. Furthermore, in this modification, the weight of daily necessities 100 can be estimated based on sensing data from weight sensor 220 of sensor unit 200, as in the first embodiment.
　＜５．２　変形例２＞
　（情報処理システム）
　さらに、本開示の実施形態に係る情報処理システム１０は、図３に示すような構成例に限定されるものではない。例えば、本開示においては、情報処理システム１０は、上述したユーザの疾患の状態のスコアや当該スコアに基づく評価結果を活用することができる各種サービス提供者が管理するサーバを含んでもよい。以下、本変形例に係る情報処理システム１０ｂの構成の一例について、図１７を参照して説明する。図１７は、本変形例に係る情報処理システム１０ｂの概略的な機能構成を示したシステム図である。 <5.2 Modification 2>
(information processing system)
Furthermore, the information processing system 10 according to the embodiment of the present disclosure is not limited to the configuration example shown in FIG. 3 . For example, in the present disclosure, the information processing system 10 may include servers managed by various service providers that can utilize the user's disease state scores and evaluation results based on the scores described above. An example of the configuration of an information processing system 10b according to this modified example will be described below with reference to FIG. FIG. 17 is a system diagram showing a schematic functional configuration of an information processing system 10b according to this modification.
　図１７に示すように、本変形例に係る情報処理システム１０ｂは、生命保険会社／健康保険組合等が管理するサーバ６００ａや、健康食品販売等のサービスをユーザに提供するサービス提供会社の管理するサーバ６００ｂや、医薬品等を開発するためのデータベース等を構築する解析会社の管理するサーバ６００等を含むことができる。 As shown in FIG. 17, an information processing system 10b according to this modification includes a server 600a managed by a life insurance company/health insurance association or the like, or a server 600a managed by a service providing company that provides users with services such as health food sales. A server 600b and a server 600 managed by an analysis company that constructs databases and the like for developing pharmaceuticals and the like can be included.
　例えば、生命保険会社／健康保険組合等が管理するサーバ６００ａは、サーバ４００からの各ユーザのスコア、評価結果、属性情報、センシングデータ等を解析し、各ユーザの保険料や報奨を特定する。特定には、生命保険会社や健康保険会社の従業員等の作業、判断等が介在してもよい。さらに、保険料の値下げや限定プランへの変更等が行われてもよい。そして、サーバ６００ａは、特定された各ユーザの保険料や報奨をユーザ端末３００等へ送信する。 For example, a server 600a managed by a life insurance company/health insurance association or the like analyzes each user's score, evaluation results, attribute information, sensing data, etc. from the server 400, and identifies each user's insurance premiums and rewards. The identification may involve the work, judgment, etc. of an employee of a life insurance company or a health insurance company. Furthermore, the insurance premium may be reduced, a change to a limited plan, or the like may be performed. Then, the server 600a transmits the specified insurance premiums and rewards for each user to the user terminal 300 and the like.
　例えば、サーバ６００ｂを管理するサービス提供会社は、ユーザに向けて、車椅子、リハビリ機器、健康食品、健康器具、ユーザの通院のための交通手段、ユーザ端末３００において実行可能な健康アプリケーション等を提供する会社である。サーバ６００ｂは、サーバ４００からの各ユーザのスコア、評価結果、属性情報、センシングデータ等を解析し、各ユーザへの報奨サービスを特定する。そして、サーバ６００ｂは、特定された各ユーザの報奨サービスの内容をユーザ端末３００等へ送信する。 For example, a service provider that manages the server 600b provides users with wheelchairs, rehabilitation equipment, health food, health equipment, means of transportation for the user to visit hospitals, health applications that can be executed on the user terminal 300, and the like. Company. The server 600b analyzes each user's score, evaluation result, attribute information, sensing data, etc. from the server 400, and specifies reward services for each user. Then, the server 600b transmits the content of the reward service identified for each user to the user terminal 300 or the like.
　また、例えば、解析会社の管理するサーバ６００ｃは、サーバ４００からの各ユーザのスコア、評価結果、属性情報、センシングデータ等を解析して、医薬品等を開発するためのデータベース等を構築することができる。なお、サーバ４００からサーバ６００ｃへ提供される各種情報は、サーバ４００において匿名化することが好ましい。このようにすることで、各情報に紐づけられたユーザを特定することができないようになるため、ユーザのプライバシーを守ることができる。さらに、サーバ６００ｃは、構築したデータベース等をサーバ４００で利用可能な情報としてサーバ４００へ送信してもよい。さらに、サーバ６００ｃは、医薬品等を開発するためのデータベース等を構築することに限定されるものではなく、健康食品、健康器具等の製品のユーザ層の分析、臨床開発のためのデータ分析等を行ってもよい。 Also, for example, a server 600c managed by an analysis company can analyze each user's score, evaluation results, attribute information, sensing data, etc. from the server 400, and construct a database or the like for developing pharmaceuticals or the like. can. Various information provided from the server 400 to the server 600c is preferably anonymized at the server 400. FIG. By doing so, it becomes impossible to specify the user associated with each piece of information, so the user's privacy can be protected. Furthermore, the server 600c may transmit the constructed database or the like to the server 400 as information that can be used by the server 400. FIG. Furthermore, the server 600c is not limited to constructing a database or the like for developing pharmaceuticals, etc., but also analyzes user groups for products such as health foods and health appliances, and data analysis for clinical development. you can go
　なお、上記サーバ６００で行われる機能の一部は、サーバ４００で実施されてもよく、もしくは、上記サーバ６００は、サーバ４００と一体の装置として構成されてもよい。 Some of the functions performed by the server 600 may be performed by the server 400, or the server 600 may be configured as a device integrated with the server 400.
　（サーバ６００）
　次に、図１８を参照して、本実施形態に係るサーバ６００の基本的構成について説明する。図１８は、本変形例に係るサーバ６００の機能構成例を示すブロック図である。詳細には、サーバ６００は、図１８に示すように、処理部６３０と、入力部６５０と、出力部６６０と、通信部６７０と、記憶部６８０とを主に有する。以下に、サーバ６００の有する各機能部について説明する。 (Server 600)
Next, with reference to FIG. 18, the basic configuration of the server 600 according to this embodiment will be described. FIG. 18 is a block diagram showing a functional configuration example of the server 600 according to this modification. Specifically, the server 600 mainly includes a processing unit 630, an input unit 650, an output unit 660, a communication unit 670, and a storage unit 680, as shown in FIG. Each functional unit of the server 600 will be described below.
　処理部６３０は、サーバ６００の各ブロックを制御することができ、例えば、ＣＰＵ、ＲＯＭ、ＲＡＭ等のハードウェアにより実現される。入力部６５０は、サーバ６００へのデータ、コマンドの入力を受け付けることができ、例えば、タッチパネル、キーボード等により実現される。出力部６６０は、例えば、ディスプレイ等により構成され、画像等により各種の情報を出力することができる。通信部６７０は、サーバ４００等の外部装置との間で情報の送受信を行うことができ、例えば、通信アンテナ、送受信回路やポート等の通信デバイスにより実現される。記憶部６８０は、上述した処理部６３０が各種処理を実行するためのプログラム、情報等や、処理によって得た情報を格納することができ、例えば、ハードディスク等の磁気記録媒体や、フラッシュメモリ等の不揮発性メモリ等により実現される。 The processing unit 630 can control each block of the server 600, and is implemented by hardware such as a CPU, ROM, and RAM, for example. The input unit 650 can receive input of data and commands to the server 600, and is realized by, for example, a touch panel, a keyboard, and the like. The output unit 660 is configured by, for example, a display or the like, and can output various kinds of information in the form of images or the like. The communication unit 670 can transmit and receive information to and from an external device such as the server 400, and is implemented by a communication device such as a communication antenna, a transmission/reception circuit, or a port, for example. The storage unit 680 can store programs, information, and the like for the processing unit 630 to execute various types of processing, and information obtained by the processing. It is implemented by a non-volatile memory or the like.
　なお、本実施形態に係るサーバ６００は、図１８に示される構成例に限定されるものではなく、例えば、他の機能部をさらに含んでいてもよい。 Note that the server 600 according to the present embodiment is not limited to the configuration example shown in FIG. 18, and may further include other functional units, for example.
　＜５．３　変形例３＞
　さらに、本開示は、筋ジストロフィー等のユーザの疾患に関するスコア（判断指標）を推定することに限定されるものではなく、例えば、フレイルや各種スポーツによるスポーツ疾患や、特定の作業を繰り返すことによる職業性疾患等の判断指標を推定する際に使用することができる。すなわち、本開示の技術によれば、様々なユーザの状態に関する判断指標を推定することが可能であるといえる。この場合、例えば、本開示の実施形態の変形例に係るテーブル４９０の一例を示す図１９のテーブル４９０をサーバ４００の記憶部４８０に格納することにより、本開示の技術は、様々なユーザの状態に関する判断指標を推定することが可能となる。 <5.3 Modification 3>
Furthermore, the present disclosure is not limited to estimating a score (judgment index) related to a user's disease such as muscular dystrophy. It can be used when estimating a judgment index such as a disease. That is, according to the technology of the present disclosure, it can be said that it is possible to estimate the determination index regarding various user states. In this case, for example, by storing the table 490 of FIG. 19 showing an example of the table 490 according to the modified example of the embodiment of the present disclosure in the storage unit 480 of the server 400, the technology of the present disclosure can be used in various user states. It is possible to estimate a judgment index for
　＜＜６．　まとめ＞＞
　以上のように、本開示の実施形態によれば、ユーザに負担をかけることなく、日常生活の中でユーザの動作を計測し、ユーザの疾患の状態等をスコア化することができる。さらに、本実施形態においては、日常的に取得された上記スコアに基づき、医療従事者やユーザ等へ早急にアラームを通知することができる。その結果、本実施形態によれば、早期発見、早期治療の機会をユーザに提供することが可能となる。 <<6. Summary>>
As described above, according to the embodiments of the present disclosure, it is possible to measure the user's motion in daily life and score the user's disease state and the like without imposing a burden on the user. Furthermore, in the present embodiment, it is possible to quickly issue an alarm to medical staff, users, etc., based on the score obtained on a daily basis. As a result, according to this embodiment, it is possible to provide the user with an opportunity for early detection and early treatment.
　＜＜７．　ハードウェア構成の例＞＞
　図２０は、ハードウェア構成の例を示すブロック図である。以下では、サーバ４００を例に挙げて説明する。ユーザ端末３００、医療従事者用端末５００、サーバ６００についても同様の説明が可能である。サーバ４００による各種処理は、ソフトウェアと、以下に説明するハードウェアとの協働により実現される。 <<7. Example of hardware configuration >>
FIG. 20 is a block diagram showing an example of hardware configuration. The server 400 will be described below as an example. The user terminal 300, the medical staff terminal 500, and the server 600 can be explained in the same way. Various types of processing by the server 400 are implemented by cooperation between software and hardware described below.
　図２０に示されるように、サーバ４００は、ＣＰＵ（Ｃｅｎｔｒａｌ　Ｐｒｏｃｅｓｓｉｎｇ　Ｕｎｉｔ）９０１、ＲＯＭ（Ｒｅａｄ　Ｏｎｌｙ　Ｍｅｍｏｒｙ）９０２、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　Ｍｅｍｏｒｙ）９０３及びホストバス９０４ａを有する。また、サーバ４００は、ブリッジ９０４、外部バス９０４ｂ、インタフェース９０５、入力装置９０６、出力装置９０７、ストレージ装置９０８、ドライブ９０９、接続ポート９１１、及び通信装置９１３を有する。サーバ４００は、ＣＰＵ９０１に代えて、又は、これとともに、ＤＳＰ（Ｄｉｇｉｔａｌ　Ｓｉｇｎａｌ　Ｐｒｏｃｅｓｓｏｒ）もしくは、ＡＳＩＣ（Ａｐｐｌｉｃａｔｉｏｎ　Ｓｐｅｃｉｆｉｃ　Ｉｎｔｅｇｒａｔｅｄ　Ｃｉｒｃｕｉｔ）等の処理回路を有してもよい。 As shown in FIG. 20, the server 400 has a CPU (Central Processing Unit) 901, a ROM (Read Only Memory) 902, a RAM (Random Access Memory) 903, and a host bus 904a. The server 400 also has a bridge 904 , an external bus 904 b , an interface 905 , an input device 906 , an output device 907 , a storage device 908 , a drive 909 , a connection port 911 and a communication device 913 . The server 400 may have a processing circuit such as a DSP (Digital Signal Processor) or an ASIC (Application Specific Integrated Circuit) in place of or in addition to the CPU 901 .
　ＣＰＵ９０１は、演算処理装置及び制御装置として機能し、各種プログラムに従ってサーバ４００内の動作全般を制御する。また、ＣＰＵ９０１は、マイクロプロセッサであってもよい。ＲＯＭ９０２は、ＣＰＵ９０１が使用するプログラムや演算パラメータ等を記憶する。ＲＡＭ９０３は、ＣＰＵ９０１の実行において使用するプログラムや、その実行において適宜変化するパラメータ等を一時記憶する。ＣＰＵ９０１は、例えば、サーバ４００の処理部４３０等を具現し得る。 The CPU 901 functions as an arithmetic processing device and a control device, and controls general operations within the server 400 according to various programs. Alternatively, the CPU 901 may be a microprocessor. The ROM 902 stores programs, calculation parameters, and the like used by the CPU 901 . The RAM 903 temporarily stores programs used in the execution of the CPU 901, parameters that change as appropriate during the execution, and the like. The CPU 901 can embody the processing unit 430 of the server 400, for example.
　ＣＰＵ９０１、ＲＯＭ９０２及びＲＡＭ９０３は、ＣＰＵバス等を含むホストバス９０４ａにより相互に接続されている。ホストバス９０４ａは、ブリッジ９０４を介して、ＰＣＩ（Ｐｅｒｉｐｈｅｒａｌ　Ｃｏｍｐｏｎｅｎｔ　Ｉｎｔｅｒｃｏｎｎｅｃｔ／Ｉｎｔｅｒｆａｃｅ）バス等の外部バス９０４ｂに接続されている。なお、ホストバス９０４ａ、ブリッジ９０４及び外部バス９０４ｂは、お互いから分離した構成を必ずしも有する必要はなく、単一の構成（例えば１つのバス）において実装されてもよい。 The CPU 901, ROM 902 and RAM 903 are interconnected by a host bus 904a including a CPU bus and the like. The host bus 904a is connected via a bridge 904 to an external bus 904b such as a PCI (Peripheral Component Interconnect/Interface) bus. It should be noted that host bus 904a, bridge 904 and external bus 904b need not necessarily have separate configurations from each other and may be implemented in a single configuration (eg, one bus).
　入力装置９０６は、例えば、マウス、キーボード、タッチパネル、ボタン、マイクロフォン、スイッチ及びレバー等、実施者によって情報が入力される装置によって実現される。また、入力装置９０６は、例えば、赤外線やその他の電波を利用したリモートコントロール装置であってもよいし、サーバ４００の操作に対応した携帯電話やＰＤＡ（Ｐｅｒｓｏｎａｌ　Ｄｉｇｉｔａｌ　Ａｓｓｉｓｔａｎｔ）等の外部接続機器であってもよい。さらに、入力装置９０６は、例えば、上記の入力手段を用いて実施者により入力された情報に基づいて入力信号を生成し、ＣＰＵ９０１に出力する入力制御回路等を含んでいてもよい。実施者は、この入力装置９０６を操作することにより、サーバ４００に対して各種のデータを入力したり処理動作を指示したりすることができる。 The input device 906 is implemented by a device such as a mouse, keyboard, touch panel, button, microphone, switch, lever, etc., through which information is input by the practitioner. Also, the input device 906 may be, for example, a remote control device using infrared rays or other radio waves, or may be an external connection device such as a mobile phone or PDA (Personal Digital Assistant) compatible with the operation of the server 400. may Furthermore, the input device 906 may include, for example, an input control circuit that generates an input signal based on information input by the practitioner using the above input means and outputs the signal to the CPU 901 . By operating the input device 906, the practitioner can input various data to the server 400 and instruct processing operations.
　出力装置９０７は、取得した情報を実施者に対して視覚的又は聴覚的に通知することが可能な装置で形成される。このような装置として、ＣＲＴ（Ｃａｔｈｏｄｅ　Ｒａｙ　Ｔｕｂｅ）ディスプレイ装置、液晶ディスプレイ装置、プラズマディスプレイ装置、ＥＬ（Ｅｌｅｃｔｒｏ　Ｌｕｍｉｎｅｓｃｅｎｔ）ディスプレイ装置及びランプ等の表示装置や、スピーカ及びヘッドホン等の音響出力装置や、プリンタ装置等がある。 The output device 907 is formed by a device capable of visually or audibly notifying the practitioner of the acquired information. Such devices include display devices such as CRT (Cathode Ray Tube) display devices, liquid crystal display devices, plasma display devices, EL (Electro Luminescent) display devices and lamps, acoustic output devices such as speakers and headphones, and printer devices. etc.
　ストレージ装置９０８は、データ格納用の装置である。ストレージ装置９０８は、例えば、ＨＤＤ（Ｈａｒｄ　Ｄｉｓｋ　Ｄｒｉｖｅ）等の磁気記憶部デバイス、半導体記憶デバイス、光記憶デバイス又は光磁気記憶デバイス等により実現される。ストレージ装置９０８は、記憶媒体、記憶媒体にデータを記録する記録装置、記憶媒体からデータを読み出す読出し装置及び記憶媒体に記録されたデータを削除する削除装置等を含んでもよい。このストレージ装置９０８は、ＣＰＵ９０１が実行するプログラムや各種データ及び外部から取得した各種のデータ等を格納する。ストレージ装置９０８は、例えば、サーバ４００の記憶部４８０等を具現し得る。 The storage device 908 is a device for storing data. The storage device 908 is realized by, for example, a magnetic storage device such as a HDD (Hard Disk Drive), a semiconductor storage device, an optical storage device, a magneto-optical storage device, or the like. The storage device 908 may include a storage medium, a recording device that records data on the storage medium, a reading device that reads data from the storage medium, a deletion device that deletes data recorded on the storage medium, and the like. The storage device 908 stores programs executed by the CPU 901, various data, and various data acquired from the outside. The storage device 908 can embody the storage unit 480 of the server 400, for example.
　ドライブ９０９は、記憶媒体用リーダライタであり、サーバ４００に内蔵、あるいは、外付けされる。ドライブ９０９は、装着されている磁気ディスク、光ディスク、光磁気ディスク、又は、半導体メモリ等のリムーバブル記憶媒体に記録されている情報を読み出して、ＲＡＭ９０３に出力する。また、ドライブ９０９は、リムーバブル記憶媒体に情報を書き込むこともできる。 The drive 909 is a reader/writer for storage media, and is built into the server 400 or externally attached. The drive 909 reads information recorded on a removable storage medium such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, and outputs the information to the RAM 903 . Drive 909 can also write information to a removable storage medium.
　接続ポート９１１は、外部機器と接続されるインタフェースであって、例えばＵＳＢ（Ｕｎｉｖｅｒｓａｌ　Ｓｅｒｉａｌ　Ｂｕｓ）等によりデータ伝送可能な外部機器との接続口である。 The connection port 911 is an interface connected to an external device, and is a connection port with an external device capable of data transmission by, for example, USB (Universal Serial Bus).
　通信装置９１３は、例えば、ネットワーク９２０に接続するための通信デバイス等で形成された通信インタフェースである。通信装置９１３は、例えば、有線若しくは無線ＬＡＮ（Ｌｏｃａｌ　Ａｒｅａ　Ｎｅｔｗｏｒｋ）、ＬＴＥ（Ｌｏｎｇ　Ｔｅｒｍ　Ｅｖｏｌｕｔｉｏｎ）、Ｂｌｕｅｔｏｏｔｈ（登録商標）又はＷＵＳＢ（Ｗｉｒｅｌｅｓｓ　ＵＳＢ）用の通信カード等である。また、通信装置９１３は、光通信用のルータ、ＡＤＳＬ（Ａｓｙｍｍｅｔｒｉｃ　Ｄｉｇｉｔａｌ　Ｓｕｂｓｃｒｉｂｅｒ　Ｌｉｎｅ）用のルータ又は各種通信用のモデム等であってもよい。この通信装置９１３は、例えば、インターネットや他の通信機器との間で、例えばＴＣＰ／ＩＰ（Ｔｒａｎｓｍｉｓｓｉｏｎ　Ｃｏｎｔｒｏｌ　Ｐｒｏｔｏｃｏｌ／Ｉｎｔｅｒｎｅｔ　Ｐｒｏｔｏｃｏｌ）等の所定のプロトコルに則して信号等を送受信することができる。通信装置９１３は、例えば、サーバ４００の通信部４７０等を具現し得る。 The communication device 913 is, for example, a communication interface formed by a communication device or the like for connecting to the network 920 . The communication device 913 is, for example, a communication card for wired or wireless LAN (Local Area Network), LTE (Long Term Evolution), Bluetooth (registered trademark), or WUSB (Wireless USB). The communication device 913 may be a router for optical communication, a router for ADSL (Asymmetric Digital Subscriber Line), a modem for various types of communication, or the like. For example, the communication device 913 can transmit and receive signals to and from the Internet and other communication devices in accordance with a predetermined protocol such as TCP/IP (Transmission Control Protocol/Internet Protocol). The communication device 913 can embody the communication unit 470 of the server 400, for example.
　なお、ネットワーク９２０は、ネットワーク９２０に接続されている装置から送信される情報の有線又は無線の伝送路である。例えば、ネットワーク９２０は、インターネット、電話回線網、衛星通信網等の公衆回線網や、Ｅｔｈｅｒｎｅｔ（登録商標）を含む各種のＬＡＮ（Ｌｏｃａｌ　Ａｒｅａ　Ｎｅｔｗｏｒｋ）、ＷＡＮ（Ｗｉｄｅ　Ａｒｅａ　Ｎｅｔｗｏｒｋ）等を含んでもよい。また、ネットワーク９２０は、ＩＰ－ＶＰＮ（Ｉｎｔｅｒｎｅｔ　Ｐｒｏｔｏｃｏｌ－Ｖｉｒｔｕａｌ　Ｐｒｉｖａｔｅ　Ｎｅｔｗｏｒｋ）等の専用回線網を含んでもよい。 Note that the network 920 is a wired or wireless transmission path for information transmitted from devices connected to the network 920 . For example, the network 920 may include a public network such as the Internet, a telephone network, a satellite communication network, various LANs (Local Area Networks) including Ethernet (registered trademark), WANs (Wide Area Networks), and the like. Network 920 may also include a dedicated line network such as IP-VPN (Internet Protocol-Virtual Private Network).
　以上、サーバ４００の機能を実現可能なハードウェア構成例を示した。上記の各構成要素は、汎用的な部材を用いて実現されていてもよいし、各構成要素の機能に特化したハードウェアにより実現されていてもよい。従って、本開示を実施する時々の技術レベルに応じて、適宜、利用するハードウェア構成を変更することが可能である。 An example of the hardware configuration capable of realizing the functions of the server 400 has been shown above. Each component described above may be implemented using general-purpose members, or may be implemented by hardware specialized for the function of each component. Therefore, it is possible to appropriately change the hardware configuration to be used according to the technical level at which the present disclosure is implemented.
　＜＜８．　補足＞＞
　なお、先に説明した本開示の実施形態は、例えば、上記で説明したような情報処理装置又は情報処理システムで実行される情報処理方法、情報処理装置を機能させるためのプログラム、及びプログラムが記録された一時的でない有形の媒体を含みうる。また、当該プログラムをインターネット等の通信回線（無線通信も含む）を介して頒布してもよい。 <<8. Supplement >>
Note that the above-described embodiments of the present disclosure include, for example, an information processing method executed by an information processing apparatus or an information processing system as described above, a program for operating the information processing apparatus, and a program in which the program is recorded. may include non-transitory tangible media that have been processed. Also, the program may be distributed via a communication line (including wireless communication) such as the Internet.
　また、上述した本開示の実施形態の情報処理方法における各ステップは、必ずしも記載された順序に沿って処理されなくてもよい。例えば、各ステップは、適宜順序が変更されて処理されてもよい。また、各ステップは、時系列的に処理される代わりに、一部並列的に又は個別的に処理されてもよい。さらに、各ステップの処理についても、必ずしも記載された方法に沿って処理されなくてもよく、例えば、他の機能部によって他の方法により処理されていてもよい。 Also, each step in the information processing method according to the embodiment of the present disclosure described above does not necessarily have to be processed in the described order. For example, each step may be processed in an appropriately changed order. Also, each step may be partially processed in parallel or individually instead of being processed in chronological order. Furthermore, the processing of each step does not necessarily have to be processed in accordance with the described method, and may be processed by another method by another functional unit, for example.
　上記各実施形態において説明した各処理のうち、自動的に行われるものとして説明した処理の全部または一部を手動的に行うこともでき、あるいは、手動的に行われるものとして説明した処理の全部または一部を公知の方法で自動的に行うこともできる。この他、上記文書中や図面中で示した処理手順、具体的名称、各種のデータやパラメータを含む情報については、特記する場合を除いて任意に変更することができる。例えば、各図に示した各種情報は、図示した情報に限られない。 Of the processes described in each of the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all of the processes described as being performed manually Alternatively, some can be done automatically by known methods. In addition, information including processing procedures, specific names, various data and parameters shown in the above documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.
　また、図示した各装置の各構成要素は機能概念的なものであり、必ずしも物理的に図示の如く構成されていることを要しない。すなわち、各装置の分散・統合の具体的形態は図示のものに限られず、その全部または一部を、各種の負荷や使用状況などに応じて、任意の単位で機能的または物理的に分散・統合して構成することができる。 Also, each component of each device illustrated is functionally conceptual and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution and integration of each device is not limited to the one shown in the figure, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured.
　以上、添付図面を参照しながら本開示の好適な実施形態について詳細に説明したが、本開示の技術的範囲はかかる例に限定されない。本開示の技術分野における通常の知識を有する者であれば、請求の範囲に記載された技術的思想の範疇内において、各種の変更例または修正例に想到し得ることは明らかであり、これらについても、当然に本開示の技術的範囲に属するものと了解される。 Although the preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, the technical scope of the present disclosure is not limited to such examples. It is obvious that a person having ordinary knowledge in the technical field of the present disclosure can conceive of various modifications or modifications within the scope of the technical idea described in the claims. are naturally within the technical scope of the present disclosure.
　また、本明細書に記載された効果は、あくまで説明的または例示的なものであって限定的ではない。つまり、本開示に係る技術は、上記の効果とともに、または上記の効果に代えて、本明細書の記載から当業者には明らかな他の効果を奏しうる。 Also, the effects described in this specification are merely descriptive or exemplary, and are not limiting. In other words, the technology according to the present disclosure can produce other effects that are obvious to those skilled in the art from the description of this specification in addition to or instead of the above effects.
　なお、本技術は以下のような構成も取ることができる。
（１）
　情報処理装置が、
　ユーザが把持する日用品に具設された第１のセンサから、当該ユーザの動作による前記日用品の移動から生じる第１のセンシングデータを取得することと、
　前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定することと、
　を含む、
　情報処理方法。
（２）
　前記日用品は、前記ユーザが把持して使用する物品である、上記（１）に記載の情報処理方法。
（３）
　前記物品は、食器、調理道具、歯ブラシ、ヘアブラシ、ドライヤー、タオル、靴べら、洋服、帽子、カバン、筆記用具、工具、携帯端末、及び、家具のうちの少なくとも１つを含む、上記（２）に記載の情報処理方法。
（４）
　前記物品は、当該物品の内包する対象物の量の変化により、前記ユーザが把持した際の前記日用品に関する重さが変化し得る物品である、上記（２）に記載の情報処理方法。
（５）
　前記物品は、グラス、コップ、皿、水筒、鍋、ヤカン、及び、カバンのうちの少なくとも１つを含む、上記（４）に記載の情報処理方法。
（６）
　前記第１のセンサは、加速度センサ、角速度センサ、地磁気センサ、及び、気圧センサのうちの少なくとも１つを含む、上記（１）～（５）のいずれか１つに記載の情報処理方法。
（７）
　前記情報処理装置が、前記第１のセンシングデータに基づいて、前記ユーザの把持による前記日用品の移動軌跡を推定することをさらに含み、
　前記判断指標の推定は、前記移動軌跡、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて行われる、
　上記（１）～（６）のいずれか１つに記載の情報処理方法。
（８）
　前記情報処理装置が、
　前記疾患の状態を検査するための複数の検査項目に関する情報を予め記憶することと、
　前記移動軌跡、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記複数の検査項目の中から、前記ユーザの動作に紐づけ可能な前記検査項目を抽出することと、
　抽出した前記検査項目に関する情報に基づいて、前記判断指標を推定することと、
　を含む、
　上記（７）に記載の情報処理方法。
（９）
　前記情報処理装置が、
　前記移動軌跡から所定の動作を検出することと、
　検出された前記所定の動作における前記移動軌跡及び前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記判断指標を推定することと、
　を含む、
　上記（７）に記載の情報処理方法。
（１０）
　前記情報処理装置が、予め機械学習により作成したアルゴリズムを用いて、前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定することと、
を含む、
　上記（１）～（６）のいずれか１つに記載の情報処理方法。
（１１）
　前記情報処理装置が、前記第１のセンシングデータに基づいて、前記ユーザが把持した際の前記日用品に関する重さの情報を推定することを含む、上記（１）～（１０）のいずれか１つに記載の情報処理方法。
（１２）
　前記情報処理装置が、
　前記日用品に具設された第２のセンサから第２のセンシングデータを取得することと、
　前記第２のセンシングデータに基づいて、前記ユーザが把持した際の前記日用品に関する重さを推定することと、
　を含む、上記（１）～（１０）のいずれか１つに記載の情報処理方法。
（１３）
　前記第２のセンサは、圧力センサ、抵抗センサ、振動センサ、重量センサ及びフォトリフレクタセンサのうちの少なくとも１つを含む、上記（１２）に記載の情報処理方法。
（１４）
　前記情報処理装置が、
　前記日用品の重さに関する情報を前記日用品の識別情報に紐づけて予め記憶することと、
　前記識別情報を、前記第１のセンシングデータとともに取得することと、
　取得した前記識別情報に基づいて、前記ユーザが把持した際の前記日用品に関する重さの情報を推定することと、
　を含む、上記（１）～（１０）のいずれか１つに記載の情報処理方法。
（１５）
　前記情報処理装置が、
　推定された前記ユーザの疾患に関する判断指標に基づいて、前記ユーザの疾患の状態を評価して、評価結果を生成することと、
　前記評価結果を出力することと、
　を含む、上記（１）～（１４）のいずれか１つに記載の情報処理方法。
（１６）
　前記情報処理装置が、
　推定された前記判断指標を記憶することと、
　前記判断指標の経時変化に基づいて、前記ユーザの疾患の状態を評価することと、
　を含む、上記（１５）に記載の情報処理方法。
（１７）
　前記情報処理装置が前記評価結果を外部端末へ出力することを含む、上記（１５）に記載の情報処理方法。
（１８）
　ユーザが把持する日用品に具設された第１のセンサから、当該ユーザの動作による前記日用品の移動から生じる第１のセンシングデータを取得する取得部と、
　前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定する推定部と、
　を備える、
　情報処理装置。
（１９）
　コンピュータを、
　ユーザが把持する日用品に具設された第１のセンサから、当該ユーザの動作による前記日用品の移動から生じる第１のセンシングデータを取得する取得部と、
　前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定する推定部と、
　として機能させる、プログラム。
（２０）
　ユーザが把持する日用品に具設され、当該ユーザの動作による前記日用品の移動から生じる第１のセンシングデータを取得する第１のセンサと、
　前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定する情報処理装置と、
　を含む、
　情報処理システム。
（２１）
　情報処理装置が、
　ユーザが把持する日用品に具設された第１のセンサから、当該ユーザの動作による前記日用品の移動から生じる第１のセンシングデータを取得することと、
　前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの身体状態に関する判断指標を推定することと、
　を含む、
　情報処理方法。 Note that the present technology can also take the following configuration.
(1)
The information processing device
Acquiring first sensing data resulting from the movement of the daily necessities by the user's actions from a first sensor provided on the daily necessities held by the user;
estimating a judgment index related to the user's disease based on the first sensing data and weight information related to the daily necessities when held by the user;
including,
Information processing methods.
(2)
The information processing method according to (1) above, wherein the daily necessities are articles held and used by the user.
(3)
The article includes at least one of tableware, cooking utensils, toothbrushes, hairbrushes, dryers, towels, shoehorns, clothes, hats, bags, writing utensils, tools, mobile terminals, and furniture. Information processing method described.
(4)
The information processing method according to (2) above, wherein the article is an article whose weight relative to the daily necessities when held by the user can change due to a change in the amount of the object contained in the article.
(5)
The information processing method according to (4) above, wherein the article includes at least one of a glass, a cup, a plate, a water bottle, a pot, a kettle, and a bag.
(6)
The information processing method according to any one of (1) to (5) above, wherein the first sensor includes at least one of an acceleration sensor, an angular velocity sensor, a geomagnetic sensor, and an atmospheric pressure sensor.
(7)
The information processing device further includes estimating a movement trajectory of the daily item held by the user based on the first sensing data,
The determination index is estimated based on the movement trajectory and information on the weight of the daily item when held by the user.
The information processing method according to any one of (1) to (6) above.
(8)
The information processing device
pre-storing information on a plurality of test items for testing the disease state;
extracting, from among the plurality of inspection items, the inspection item that can be linked to the motion of the user based on the movement trajectory and information on the weight of the daily item when held by the user; ,
estimating the judgment index based on the extracted information about the inspection item;
including,
The information processing method according to (7) above.
(9)
The information processing device
detecting a predetermined motion from the movement trajectory;
estimating the determination index based on the detected movement trajectory in the predetermined motion and information on the weight of the daily item when held by the user;
including,
The information processing method according to (7) above.
(10)
The information processing device uses an algorithm created in advance by machine learning to determine the disease of the user based on the first sensing data and information on the weight of the daily item when held by the user. estimating an indicator;
including,
The information processing method according to any one of (1) to (6) above.
(11)
Any one of (1) to (10) above, wherein the information processing device estimates weight information about the daily item when held by the user, based on the first sensing data. The information processing method described in .
(12)
The information processing device
Acquiring second sensing data from a second sensor provided in the daily necessities;
estimating the weight of the daily necessities when held by the user based on the second sensing data;
The information processing method according to any one of (1) to (10) above, including
(13)
The information processing method according to (12) above, wherein the second sensor includes at least one of a pressure sensor, a resistance sensor, a vibration sensor, a weight sensor, and a photoreflector sensor.
(14)
The information processing device
pre-storing information about the weight of the daily necessities in association with the identification information of the daily necessities;
Acquiring the identification information together with the first sensing data;
estimating weight information about the daily necessities when held by the user based on the acquired identification information;
The information processing method according to any one of (1) to (10) above, including
(15)
The information processing device
Evaluating the disease state of the user based on the estimated judgment index related to the user's disease to generate an evaluation result;
outputting the evaluation result;
The information processing method according to any one of (1) to (14) above, including
(16)
The information processing device
storing the estimated decision indicator;
Evaluating the user's disease state based on the change over time of the decision index;
The information processing method according to (15) above, comprising:
(17)
The information processing method according to (15) above, wherein the information processing device outputs the evaluation result to an external terminal.
(18)
an acquisition unit that acquires first sensing data generated from movement of the daily necessities by the user's motion from a first sensor provided on the daily necessities held by the user;
an estimating unit for estimating a judgment index related to the user's disease based on the first sensing data and weight information related to the daily necessities when held by the user;
comprising
Information processing equipment.
(19)
the computer,
an acquisition unit that acquires first sensing data generated from movement of the daily necessities by the user's motion from a first sensor provided on the daily necessities held by the user;
an estimating unit for estimating a judgment index related to the user's disease based on the first sensing data and weight information related to the daily necessities when held by the user;
A program that functions as
(20)
a first sensor attached to a daily item held by a user and acquiring first sensing data generated from movement of the daily item by the user's motion;
an information processing device for estimating a judgment index related to the user's disease based on the first sensing data and weight information related to the daily necessities when held by the user;
including,
Information processing system.
(21)
The information processing device
Acquiring first sensing data resulting from the movement of the daily necessities by the action of the user from a first sensor provided on the daily necessities held by the user;
estimating a judgment index relating to the user's physical condition based on the first sensing data and information about the weight of the daily item when held by the user;
including,
Information processing methods.
　　１０、１０ａ、１０ｂ　　情報処理システム
　　１００、１００ａ、１００ｂ、１００ｃ　　日用品
　　１０２　　マーカ
　　２００、２００ａ　　センサユニット
　　２１０　　ＩＭＵ
　　２２０　　重量センサ
　　２３０、３３０、５３０　　制御部
　　２７０、３７０、４７０、５７０、６７０　　通信部
　　２９０　　カメラ
　　３００　　ユーザ端末
　　４３０、６３０　　処理部
　　３５０、４５０、５５０、６５０　　入力部
　　３６０、４６０、５６０、６６０　　出力部
　　３８０、４８０、５８０、６８０　　記憶部
　　４００、６００、６００ａ、６００ｂ、６００ｃ　　サーバ
　　４３２　　取得部
　　４３４　　推定部
　　４３６　　評価部
　　４３８　　情報出力部
　　４４０　　学習器
　　４８２　　検査項目テーブル
　　４８４　　判定テーブル
　　４８６　　ユーザデータ
　　４８８　　推定モデル
　　４９０　　テーブル
　　５００　　医療従事者用端末
　　８００　　ネットワーク Reference Signs List 10, 10a, 10b Information processing system 100, 100a, 100b, 100c Daily necessities 102 Marker 200, 200a Sensor unit 210 IMU
220 weight sensor 230, 330, 530 control section 270, 370, 470, 570, 670 communication section 290 camera 300 user terminal 430, 630 processing section 350, 450, 550, 650 input section 360, 460, 560, 660 output section 380 , 480, 580, 680 storage unit 400, 600, 600a, 600b, 600c server 432 acquisition unit 434 estimation unit 436 evaluation unit 438 information output unit 440 learning device 482 inspection item table 484 determination table 486 user data 488 estimation model 490 table 500 Medical staff terminal 800 network

Claims (20)

  1. 　情報処理装置が、
    　ユーザが把持する日用品に具設された第１のセンサから、当該ユーザの動作による前記日用品の移動から生じる第１のセンシングデータを取得することと、
    　前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定することと、
    　を含む、
    　情報処理方法。     The information processing device
    Acquiring first sensing data resulting from the movement of the daily necessities by the user's actions from a first sensor provided on the daily necessities held by the user;
    estimating a judgment index related to the user's disease based on the first sensing data and weight information related to the daily necessities when held by the user;
    including,
    Information processing methods.
  2. 　前記日用品は、前記ユーザが把持して使用する物品である、請求項１に記載の情報処理方法。 The information processing method according to claim 1, wherein the daily necessities are articles that are held and used by the user.
  3. 　前記物品は、食器、調理道具、歯ブラシ、ヘアブラシ、ドライヤー、タオル、靴べら、洋服、帽子、カバン、筆記用具、工具、携帯端末、及び、家具のうちの少なくとも１つを含む、請求項２に記載の情報処理方法。 3. The article according to claim 2, wherein the articles include at least one of tableware, cooking utensils, toothbrushes, hairbrushes, dryers, towels, shoehorns, clothes, hats, bags, writing utensils, tools, portable terminals, and furniture. information processing method.
  4. 　前記物品は、当該物品の内包する対象物の量の変化により、前記ユーザが把持した際の前記日用品に関する重さが変化し得る物品である、請求項２に記載の情報処理方法。 The information processing method according to claim 2, wherein the article is an article whose weight related to the daily necessities when held by the user can change due to a change in the amount of the object contained in the article.
  5. 　前記物品は、グラス、コップ、皿、水筒、鍋、ヤカン、及び、カバンのうちの少なくとも１つを含む、請求項４に記載の情報処理方法。 The information processing method according to claim 4, wherein the articles include at least one of glasses, cups, plates, water bottles, pots, kettles, and bags.
  6. 　前記第１のセンサは、加速度センサ、角速度センサ、地磁気センサ、及び、気圧センサのうちの少なくとも１つを含む、請求項１に記載の情報処理方法。 The information processing method according to claim 1, wherein the first sensor includes at least one of an acceleration sensor, an angular velocity sensor, a geomagnetic sensor, and an atmospheric pressure sensor.
  7. 　前記情報処理装置が、前記第１のセンシングデータに基づいて、前記ユーザの把持による前記日用品の移動軌跡を推定することをさらに含み、
    　前記判断指標の推定は、前記移動軌跡、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて行われる、
    　請求項１に記載の情報処理方法。     The information processing device further includes estimating a movement trajectory of the daily item held by the user based on the first sensing data,
    The determination index is estimated based on the movement trajectory and information on the weight of the daily item when held by the user.
    The information processing method according to claim 1 .
  8. 　前記情報処理装置が、
    　前記疾患の状態を検査するための複数の検査項目に関する情報を予め記憶することと、
    　前記移動軌跡、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記複数の検査項目の中から、前記ユーザの動作に紐づけ可能な前記検査項目を抽出することと、
    　抽出した前記検査項目に関する情報に基づいて、前記判断指標を推定することと、
    　を含む、
    　請求項７に記載の情報処理方法。     The information processing device
    pre-storing information on a plurality of test items for testing the disease state;
    extracting, from among the plurality of inspection items, the inspection item that can be linked to the motion of the user based on the movement trajectory and information on the weight of the daily item when held by the user; ,
    estimating the judgment index based on the extracted information about the inspection item;
    including,
    The information processing method according to claim 7.
  9. 　前記情報処理装置が、
    　前記移動軌跡から所定の動作を検出することと、
    　検出された前記所定の動作における前記移動軌跡及び前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記判断指標を推定することと、
    　を含む、
    　請求項７に記載の情報処理方法。     The information processing device
    detecting a predetermined motion from the movement trajectory;
    estimating the determination index based on the detected movement trajectory in the predetermined motion and information on the weight of the daily item when held by the user;
    including,
    The information processing method according to claim 7.
  10. 　前記情報処理装置が、予め機械学習により作成したアルゴリズムを用いて、前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定することと、
    を含む、
    　請求項１に記載の情報処理方法。     The information processing device uses an algorithm created in advance by machine learning to determine the disease of the user based on the first sensing data and information on the weight of the daily item when held by the user. estimating an indicator;
    including,
    The information processing method according to claim 1 .
  11. 　前記情報処理装置が、前記第１のセンシングデータに基づいて、前記ユーザが把持した際の前記日用品に関する重さの情報を推定することを含む、請求項１に記載の情報処理方法。 The information processing method according to claim 1, wherein the information processing device estimates weight information about the daily necessities when held by the user, based on the first sensing data.
  12. 　前記情報処理装置が、
    　前記日用品に具設された第２のセンサから第２のセンシングデータを取得することと、
    　前記第２のセンシングデータに基づいて、前記ユーザが把持した際の前記日用品に関する重さを推定することと、
    　を含む、請求項１に記載の情報処理方法。     The information processing device
    Acquiring second sensing data from a second sensor provided in the daily necessities;
    estimating the weight of the daily necessities when held by the user based on the second sensing data;
    The information processing method according to claim 1, comprising:
  13. 　前記第２のセンサは、圧力センサ、抵抗センサ、振動センサ、重量センサ及びフォトリフレクタセンサのうちの少なくとも１つを含む、請求項１２に記載の情報処理方法。 13. The information processing method according to claim 12, wherein said second sensor includes at least one of a pressure sensor, a resistance sensor, a vibration sensor, a weight sensor and a photoreflector sensor.
  14. 　前記情報処理装置が、
    　前記日用品の重さに関する情報を前記日用品の識別情報に紐づけて予め記憶することと、
    　前記識別情報を、前記第１のセンシングデータとともに取得することと、
    　取得した前記識別情報に基づいて、前記ユーザが把持した際の前記日用品に関する重さの情報を推定することと、
    　を含む、請求項１に記載の情報処理方法。     The information processing device
    pre-storing information about the weight of the daily necessities in association with the identification information of the daily necessities;
    Acquiring the identification information together with the first sensing data;
    estimating weight information about the daily necessities when held by the user based on the acquired identification information;
    The information processing method according to claim 1, comprising:
  15. 　前記情報処理装置が、
    　推定された前記ユーザの疾患に関する判断指標に基づいて、前記ユーザの疾患の状態を評価して、評価結果を生成することと、
    　前記評価結果を出力することと、
    　を含む、請求項１に記載の情報処理方法。     The information processing device
    Evaluating the disease state of the user based on the estimated judgment index related to the user's disease to generate an evaluation result;
    outputting the evaluation result;
    The information processing method according to claim 1, comprising:
  16. 　前記情報処理装置が、
    　推定された前記判断指標を記憶することと、
    　前記判断指標の経時変化に基づいて、前記ユーザの疾患の状態を評価することと、
    　を含む、請求項１５に記載の情報処理方法。     The information processing device
    storing the estimated decision indicator;
    Evaluating the user's disease state based on the change over time of the decision index;
    The information processing method according to claim 15, comprising:
  17. 　前記情報処理装置が前記評価結果を外部端末へ出力することを含む、請求項１５に記載の情報処理方法。 16. The information processing method according to claim 15, wherein said information processing device outputs said evaluation result to an external terminal.
  18. 　ユーザが把持する日用品に具設された第１のセンサから、当該ユーザの動作による前記日用品の移動から生じる第１のセンシングデータを取得する取得部と、
    　前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定する推定部と、
    　を備える、
    　情報処理装置。     an acquisition unit that acquires first sensing data generated from movement of the daily necessities by the user's motion from a first sensor provided on the daily necessities held by the user;
    an estimating unit for estimating a judgment index related to the user's disease based on the first sensing data and weight information related to the daily necessities when held by the user;
    comprising
    Information processing equipment.
  19. 　コンピュータを、
    　ユーザが把持する日用品に具設された第１のセンサから、当該ユーザの動作による前記日用品の移動から生じる第１のセンシングデータを取得する取得部と、
    　前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定する推定部と、
    　として機能させる、プログラム。     the computer,
    an acquisition unit that acquires first sensing data generated from movement of the daily necessities by the user's motion from a first sensor provided on the daily necessities held by the user;
    an estimating unit for estimating a judgment index related to the user's disease based on the first sensing data and weight information related to the daily necessities when held by the user;
    A program that functions as
  20. 　ユーザが把持する日用品に具設され、当該ユーザの動作による前記日用品の移動から生じる第１のセンシングデータを取得する第１のセンサと、
    　前記第１のセンシングデータ、及び、前記ユーザが把持した際の前記日用品に関する重さの情報に基づいて、前記ユーザの疾患に関する判断指標を推定する情報処理装置と、
    　を含む、
    　情報処理システム。     a first sensor attached to a daily item held by a user and acquiring first sensing data generated from the movement of the daily item by the action of the user;
    an information processing device for estimating a judgment index regarding a disease of the user based on the first sensing data and weight information regarding the daily necessities when held by the user;
    including,
    Information processing system.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014533127A (en) * 2011-09-30 2014-12-11 リンクス デザインＬｙｎｘ Ｄｅｓｉｇｎ System and method for stabilizing unintentional muscle movement
JP2017029308A (en) * 2015-07-30 2017-02-09 優 長島 Ataxia reduction device
JP2018513707A (en) * 2015-02-20 2018-05-31 ヴェリリー ライフ サイエンシズ エルエルシー Measurement and collection of human tremor through hand-held instruments
CN210114305U (en) * 2019-03-20 2020-02-28 嘉兴深拓科技有限公司 Shaking sensing and recording spoon

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014533127A (en) * 2011-09-30 2014-12-11 リンクス デザインＬｙｎｘ Ｄｅｓｉｇｎ System and method for stabilizing unintentional muscle movement
JP2018513707A (en) * 2015-02-20 2018-05-31 ヴェリリー ライフ サイエンシズ エルエルシー Measurement and collection of human tremor through hand-held instruments
JP2017029308A (en) * 2015-07-30 2017-02-09 優 長島 Ataxia reduction device
CN210114305U (en) * 2019-03-20 2020-02-28 嘉兴深拓科技有限公司 Shaking sensing and recording spoon

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