WO2018164157A1 - Walking and foot evaluation method, walking and foot evaluation program, and walking and foot evaluation device - Google Patents

Walking and foot evaluation method, walking and foot evaluation program, and walking and foot evaluation device Download PDF

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Publication number
WO2018164157A1
WO2018164157A1 PCT/JP2018/008663 JP2018008663W WO2018164157A1 WO 2018164157 A1 WO2018164157 A1 WO 2018164157A1 JP 2018008663 W JP2018008663 W JP 2018008663W WO 2018164157 A1 WO2018164157 A1 WO 2018164157A1
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WIPO (PCT)
Prior art keywords
foot
walking
data
parameter
evaluation
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PCT/JP2018/008663
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French (fr)
Japanese (ja)
Inventor
裕治 太田
絵美 安在
香奈子 中嶋
美希 留奥
直樹 才脇
安那 笹田
Original Assignee
国立大学法人お茶の水女子大学
国立研究開発法人産業技術総合研究所
国立大学法人奈良女子大学
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Application filed by 国立大学法人お茶の水女子大学, 国立研究開発法人産業技術総合研究所, 国立大学法人奈良女子大学 filed Critical 国立大学法人お茶の水女子大学
Priority to JP2019504620A priority Critical patent/JP6928355B2/en
Publication of WO2018164157A1 publication Critical patent/WO2018164157A1/en

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    • A—HUMAN NECESSITIES
    • A43—FOOTWEAR
    • A43D—MACHINES, TOOLS, EQUIPMENT OR METHODS FOR MANUFACTURING OR REPAIRING FOOTWEAR
    • A43D1/00—Foot or last measuring devices; Measuring devices for shoe parts
    • A43D1/02—Foot-measuring devices
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb

Definitions

  • the present invention relates to a walking / foot evaluation method, a walking / foot evaluation program, and a walking / foot evaluation device.
  • Patent Documents 1 and 2 In related fields, inventions relating to the development of devices for detecting human plantar pressure have been made (Patent Documents 1 and 2).
  • the foot shape is measured in a standing position, and the shoe is supported by a technique that supports shoe selection based on foot arch height data (Patent Document 3) and a sample insole with a different fitting point and raised height.
  • Patent Document 4 There has also been proposed a technique (Patent Document 4) for determining a fitting point and a raised height by comparing the walking motion data out of balance with the acceleration measured by the user.
  • Non-Patent Documents 1 to 3 development of a measuring device for evaluating plantar pressure and verification of its effectiveness (Non-Patent Document 1), investigation on the relationship between the foot arch structure and plantar pressure using the developed device (Non-Patent Document 2) , Research on the relationship between plantar pressure value characteristics and the fall history of the elderly (Non-Patent Document 3), proposal of a mechanism that can evaluate the state of the foot in daily life, and the feature value of the data obtained Have been evaluated.
  • the conventional technology was able to acquire data from individual subjects, it does not consider collecting data of multiple users in a unified manner, and performs appropriate analysis and evaluation It was not enough on.
  • the conventional technology uses measurement data in a stationary state or measurement data of a walking state in an experimentally set environment, both of which acquire data from walking movements in daily life and perform analysis / evaluation Is not realized.
  • the present invention has been proposed in view of the above-described conventional problems, the purpose of which is to grasp the daily walking state of the user as data, and further collect data of a plurality of users in a unified manner, It is to ensure that appropriate analysis and evaluation is performed.
  • At least plantar pressure data for a predetermined time during walking and standing from one or more sensors provided on an insole of a shoe used by a plurality of users. And analyzing the acquired data, and for each user, at least the plantar pressure parameter, the foot pressure center parameter and the time parameter during walking, and at least the plantar pressure parameter and the foot pressure center parameter when standing still
  • the computer executes the process of acquiring and accumulating.
  • data of a plurality of users are uniformly collected from information obtained from daily walking motions of the user, and appropriate analysis / evaluation is performed.
  • FIG. (2) which shows the structural example of various data.
  • FIG. (3) which shows the structural example of various data.
  • FIG. (4) which shows the structural example of various data.
  • FIG. (5) which shows the structural example of various data.
  • FIG. (6) which shows the structural example of various data.
  • FIG. (7) which shows the example of a structure of various data.
  • FIG. 6 is a second diagram illustrating an example of COP variation. It is a flowchart which shows the process example of a stance time evaluation part. It is a flowchart which shows the process example of a both leg support ratio evaluation part. It is a flowchart which shows the process example of a right-and-left difference evaluation part.
  • FIG. 1 is a diagram showing a configuration example of a system according to an embodiment of the present invention.
  • a shoe 1 left and right used by a user is provided with a measuring device (shoe device) 2, and signal data detected by a sensor unit 21 is transmitted via Bluetooth (registered trademark) via a communication unit 22.
  • the information is transmitted to an information terminal 3 such as a smartphone, a tablet, or a PC (Personal Computer) by wireless communication means such as a wireless local area network (LAN).
  • the data transmission interval from the measuring device 2 to the information terminal 3 is, for example, 10 ms (millisecond, 100 Hz).
  • the sensor unit 21 includes, for example, a plurality of (seven in the illustrated example) pressure sensors 212 on an insole (insole) type base material 211.
  • a shearing force (frictional force) sensor may be provided.
  • the insole is provided with a mechanism for changing color (mechanism for applying visual stimulation) or a mechanism for changing material or changing hardness (mechanism for applying tactile stimulation) by control from the information terminal 3 side. By doing so, the user may be fed back with respect to the state of walking or foot.
  • the communication unit 22 also has a function of transmitting position data by GPS (Global Positioning System) or the like. The position data may be acquired from the information terminal 3 instead of the measurement device 2.
  • the information terminal 3 transmits the data received from the measuring device 2 and temporarily accumulated to the server device 5 via a network 4 such as a mobile radio network or the Internet at predetermined time intervals (for example, 10 s). It has a function to do. In addition, the information terminal 3 acquires information on the user's walking and foot state from the server device 5 and displays the screen, and provides the user with feedback on walking and foot state, shoe selection support, and the like. It also has a function. In addition, although it was description about the case where data are transmitted to the server apparatus 5 via the information terminal 3 from the measurement device 2, data are directly transmitted from the measurement device 2 to the server apparatus 5 depending on an environment. It may be a thing. In this case, the information terminal 3 is used for the operation of the measuring device 2 and feedback to the user.
  • a network 4 such as a mobile radio network or the Internet at predetermined time intervals (for example, 10 s). It has a function to do.
  • the information terminal 3 acquires information on the user's walking and foot state from the server
  • the server device 5 includes a basic data input unit 501, a measurement data receiving unit 502, a data analysis unit 503, a life log writing unit 506, an evaluation unit 507, a comprehensive evaluation unit 515, and a comprehensive analysis unit 516 as parts for realizing processing functions. And. Further, a database 521 for accumulating data used for processing is provided. The database 521 may be held and managed separately from the server device 5.
  • the database 521 holds user data 522, shoe data 523, life log data 524, position data 525, measurement data 526, post-walking processing data 527, and stationary post-processing data 528.
  • User data 522 and shoe data 523 are basic data such as users and shoes.
  • the life log data 524 is data indicating user behavior (including a schedule).
  • the position data 525 is data acquired from the measurement device 2 or the information terminal 3
  • the measurement data 526 is data acquired from the measurement device 2.
  • the post-walking processing data 527 and the stationary standing processing post-processing data 528 are data obtained by analysis from the measurement data 526.
  • the user data 522 includes “user ID”, “name”, “shoe ID”, “sex”, “birth date”, “height”, “weight”, “shoe size”, “foot length”, “foot width”. ”,“ Foot height ”,“ foot circumference ”,“ registration date ”, and“ update date ”.
  • the shoe data 523 includes “shoe ID”, “user ID”, “purchase date”, “use start date”, “shoe store ID”, “shoe manufacturer model number”, “shoe type”, “theme”, “shoe rub”.
  • the life log data 524 includes “log ID”, “year / month / day / time”, “user ID”, “plan for one day”, “destination”, “movement distance”, “step count”, and “average walking speed”. ”,“ Most position information (GPS) ”,“ registration date ”,“ update date ”, and the like.
  • the position data 525 has items such as “year / month / day / time”, “user ID”, and “position information (GPS)” as illustrated in FIG.
  • the measurement data 526 includes “year / month / day / time”, “user ID”, “left foot 1 sensor: hind foot (heel) pressure value”, “left foot 2 sensor: middle foot 1). “Pressure value” “Left foot No. 3 sensor: Forefoot 1) Pressure value” “Left foot No. 4 sensor: Forefoot 2 pressure value” “Left foot No. 5 sensor: Forefoot 3 pressure value” “Left foot No.
  • the sensor numbers are shown in FIG.
  • the waveform data is continuous data of pressure values for a time (for example, 10 s) accumulated in the information terminal 3 in a sampling period (for example, 10 ms).
  • COP is the center of foot pressure.
  • Left foot first sensor: rear foot ( ⁇ ) pressure value indicate waveform data of the pressure value.
  • Bottom foot pressure center COP_X coordinate value indicates a locus of coordinate values.
  • the post-walking data 527 includes “year / month / day time”, “user ID”, “number of steps”, “left leg average stance time”, “right leg average stance time”, “both leg support ratio”, “left leg monopod”.
  • "Support ratio” "Right foot single leg support ratio”
  • Right foot 3rd sensor Forefoot 1 maximum pressure value average”“ Right foot 4 sensor
  • Left foot No. 1 sensor rear foot ( ⁇ ) maximum pressure value average” and the like are plantar pressure parameters.
  • Left foot COP bending angle “Left foot walking middle period COP_X coordinate range” and the like are COP parameters.
  • Left foot average stance time “right foot average stance time”, “both leg support ratio”, “left foot single leg support ratio”, and “right foot single leg support ratio” are time parameters.
  • the post-stillation processing data 528 includes “year / month / day / time”, “user ID”, “left foot 1 sensor: rear foot (heel) load ratio”, “left foot 2 sensor: middle”.
  • “1st foot load ratio” "Left foot 3 sensor: front foot 1 load ratio”
  • Left foot 4 sensor front foot 2 load ratio”
  • Left foot 5 sensor front foot 3 load ratio
  • Left foot 6 sensor middle foot Section 2 load ratio ""
  • Right foot 3 sensor Middle foot 1 load ratio ""
  • Right foot 3 sensor “Forefoot 1 load ratio”
  • Right foot 6 sensor Middle foot 2 load ratio”
  • the basic data input unit 501 has a function of accepting basic data settings such as users and shoes and registering them in the user data 522 and shoe data 523 of the database 521, respectively.
  • the measurement data receiving unit 502 has a function of receiving data transmitted from the measurement device 2 via the information terminal 3 and registering it in the position data 525 and the measurement data 526 of the database 521.
  • the data analysis unit 503 includes a stance phase data analysis unit 504 and a stationary standing data analysis unit 505, and has a function of generating data serving as a basis for evaluation by the subsequent evaluation unit 507. That is, the stance phase data analysis unit 504 analyzes the measurement data 526 and generates post-walking data 527, and the stationary standing data analysis unit 505 analyzes the measurement data 526 and generates post-resting processing data 528. .
  • the life log writing unit 506 has a function of recording in the position data 525 and the life log data 524 of the database 521.
  • the evaluation unit 507 includes a forefoot local load evaluation unit 508, a pronation foot / extraction foot evaluation unit 509, a multi-step balance evaluation unit 510, a stance time evaluation unit 511, a both-leg support ratio evaluation unit 512, and a left-right difference evaluation unit 513.
  • a foot arch evaluation unit 514 and a buckle contact evaluation unit 517 are provided and have a function of performing various evaluations and generating an evaluation result.
  • the evaluation unit 507 evaluates each time, the evaluation result may be stored in association with the user.
  • the comprehensive evaluation unit 515 calculates a degree of leg, a body balance, a walking rhythm, and an activity from various evaluation results by the evaluation unit 507 and the life log data 524, and performs a comprehensive evaluation of the user's walking / foot. It has a function of visualizing and feeding back to the user.
  • the comprehensive analysis unit 516 has a function of performing a comprehensive analysis based on data accumulated in the database 521 and taking into account temporal changes, and realizing early detection / prediction warning of foot abnormalities. For example, even if the possibility of occurrence of a disorder or the like has not been evaluated for the target user at this time, the foot / still standing state and walking function are similar from other users' past data, When the possibility of the occurrence of a failure or the like is subsequently evaluated by the user, the occurrence of the failure or the like is also estimated in the future for the target user, and that fact is fed back.
  • the comprehensive analysis unit 516 also has a function of providing advice on selecting shoes suitable for the individual user and suggesting how to walk.
  • the management terminal 6 operated by the administrator can be connected to the server device 5 via the network 4 so that the administrator can confirm and maintain data managed by the server device 5.
  • FIG. 10 is a diagram illustrating a hardware configuration example related to information processing of the measurement device 2, the information terminal 3, the server device 5, and the management terminal 6, and illustrates a general computer configuration.
  • a measurement device 2 and the like are a CPU (Central Processing Unit) 201, a ROM (Read Only Memory) 202, a RAM (Random Access Memory) 203, and an SSD (Solid State Drive) that are connected to each other via a bus 207.
  • / HDD (Hard Disk Disk Drive) 204 is provided.
  • the measuring device 2 and the like include a connection I / F (Interface) 205 and a communication I / F 206.
  • the CPU 201 performs overall control of the operation of the measurement device 2 and the like by executing a program stored in the ROM 202 or the SSD / HDD 204 or the like using the RAM 203 as a work area.
  • the functions of the measurement device 2 and the like described in FIG. 1 are realized by executing a predetermined program in the CPU 201.
  • the program may be acquired via a recording medium, may be acquired via a network, or may be embedded in a ROM.
  • the stance phase data analysis unit 504 analyzes the measurement data 526 acquired during stance walking and generates post-walking processing data 527. In other words, the stance phase data analysis unit 504 uses the waveform data representing the human walking motion to perform walking events such as the stance phase and the free leg phase (when one foot of the human walking motion is seen, the foot touches the ground. Automatically detected in the stance phase in the separated state and the free leg phase in the state of getting out of the ground) to obtain numerical values related to human walking motion.
  • FIG. 11 is a flowchart illustrating a processing example of the stance phase data analysis unit 504.
  • the stance phase data analysis unit 504 acquires waveform data of pressure values of the respective sensors in the measurement data 526 (step S111), detects a ground contact point from the rising edge of the waveform, and cuts out waveform data for each walk. (Step S112). That is, the state from the point where the pressure values of all the sensors become the minimum to the point where the pressure values of all the sensors become the next is set as one stance phase, but the stance phase data is data of a normal walking state. This is scrutinized under the following conditions, and data corresponding to this is defined as one stance phase used for parameter extraction.
  • a sensor showing the highest pressure value (top maximum value) in the entire waveform data for a fixed period of walking is specified, and a plurality of maximum values of the waveform data from the sensor are all 80% based on the top maximum value.
  • B The length of one stance phase is less than 1200 ms.
  • FIG. 12 shows an example of the waveform data of seven sensors, and the waveforms of a plurality of steps are recorded. From these, the waveform data for each walk as shown in FIG. 13 is cut out.
  • the stance phase data analysis unit 504 detects the maximum pressure value of the waveform for each walk of each sensor, and divides the total maximum pressure value of a plurality of steps for each sensor by the number of steps. "Left foot No. 1 sensor: hind foot ( ⁇ ) maximum pressure value average” etc.) is acquired and acquired as a plantar pressure parameter (step S113).
  • FIG. 14 shows a state in which the maximum value (illustrated by a black circle) is acquired from the waveform of each sensor.
  • the stance phase data analysis unit 504 then includes the “left foot foot pressure center COP_X coordinate value”, “left foot foot pressure center COP_Y coordinate value”, “right foot foot pressure center COP_X coordinate value”, and “right foot foot pressure” in the measurement data 526.
  • center COP_Y coordinate value as COP parameters, “COP start position X coordinate” “COP start position Y coordinate” “COP end position X coordinate” “COP end position Y coordinate” “left foot COP flex angle” “right foot COP flex angle” "" Left-foot walking middle period COP_X coordinate range "" "Right-foot walking middle period COP_X coordinate range” is acquired (step S114).
  • FIG. 15 shows a COP trajectory passing from point A, which is the COP start position, through point H, (the intersection of the straight line connecting the third sensor and the fifth sensor and the COP trajectory) and passing through point B, which is the COP end position.
  • the angle formed by the straight line AH and the straight line HB is the COP bending angle.
  • the lateral direction in a plurality of steps of the COP coordinate closest to the midpoint of the COP start position Y coordinate and the COP end position Y coordinate intermediate between ground contact and getting out of bed
  • the Y coordinate direction that is the walking direction (longitudinal direction of the sole)
  • the difference between the maximum value and the minimum value is the middle walking COP_X coordinate range.
  • the stance phase data analysis unit 504 then, as a time parameter, “left foot average stance time”, “right foot average stance time”, “both leg support ratio”, “left foot single leg support ratio”, which is an average of a plurality of steps.
  • "Right foot single leg support ratio” is acquired (step S115).
  • FIG. 16 shows the ground contact state of the left and right feet. The time from one foot to the floor is the standing time, the time when both feet are grounded is the both-leg support period, and the time when only one foot is grounded is the single-leg support. It is a period.
  • the both-leg support ratio is a ratio of the time of the both-leg support period in the stance time.
  • the monopod support ratio is a ratio of the time of the single leg support period in the stance time.
  • the stance phase data analysis unit 504 then walks the acquired plantar pressure parameter, COP parameter, and time parameter, and the number of steps that are the number of stance phases within a predetermined walking time used in the analysis. It records in the post-processing data 527 (step S116).
  • the static standing data analysis unit 505 analyzes the measurement data 526 acquired when the standing posture is maintained for a certain period, and generates post-static standing processing data 528.
  • FIG. 17 is a flowchart showing a processing example of the stationary standing data analysis unit 505.
  • the stationary standing data analysis unit 505 acquires waveform data of the pressure value of each sensor of the measurement data 526 (step S121), and COP trajectory data (“both foot foot pressure center COP_X coordinate value” “both foot foot pressure”.
  • the center COP_Y coordinate value ”) is acquired (step S122).
  • the COP trajectory here is in a state of standing on both feet, and is therefore located near the center of both feet.
  • FIG. 18 shows an example of a COP locus, which fluctuates slightly toward the right foot.
  • the stationary standing data analysis unit 505 acquires the COP total trajectory length as one of the COP parameters from the COP trajectory (step S123). That is, the minute movement amount is obtained from the displacement of the COP x-coordinate and y-coordinate for each sampling period of the measurement data 526, and the total movement length is obtained by integrating the minute movement amount.
  • the stationary standing data analysis unit 505 acquires a COP rectangular area as one of the COP parameters (step S124).
  • the COP rectangular area is obtained as an area of a circumscribed rectangle of the COP locus.
  • the static standing data analysis unit 505 acquires the load ratio of each sensor as one of the plantar pressure parameters (step S125).
  • the load ratio of each sensor is obtained by dividing the average pressure value of each sensor by the sum of the average pressure values of all sensors (14 sensors on both feet).
  • FIG. 18 shows an example of load ratio values for each sensor (illustrated by a circle).
  • the stationary standing data analysis unit 505 then records the acquired plantar pressure parameter and COP parameter in the stationary standing post-processing data 528 (step S ⁇ b> 126).
  • the forefoot local load evaluating unit 508 evaluates the presence or absence of a local load on the forefoot part based on the post-walking data 527, and whether or not there is a possibility of occurrence of abnormalities such as eyelids or fish eyes.
  • FIG. 19 is a flowchart showing a processing example of the forefoot local load evaluation unit 508, which is an example of evaluating the possibility of wrinkles by evaluating the presence or absence of a local load on the forefoot.
  • the presence or absence of a local load is evaluated by comparing the values of each sensor based on the average of the maximum pressure values from the three sensors on the forefoot.
  • the forefoot local load evaluating unit 508 refers to the maximum pressure value average of the forefoot (3rd, 5th, and 7th sensors) from the post-walking data 527 (step S211).
  • the forefoot local load evaluating unit 508 sets the sensor indicating the maximum value among the three sensors and its value as S, and the other two sensors and their values as a and b (step S212).
  • the forefoot local load evaluating unit 508 determines the load ratio between S and 2a, and S and 2b (step S213).
  • the forefoot local load evaluating unit 508 When the forefoot local load evaluating unit 508 does not satisfy the condition that S is greater than 2a and S is greater than 2b (NO in step S213), the forefoot local load evaluating unit 508 assumes a normal load (step S214).
  • the forefoot local load evaluating unit 508 has a local load on the sensor S (step S215). It is evaluated that there is a possibility of occurrence of wrinkles in the area of the sensor S (step S216).
  • the pronation / extroversion foot evaluation unit 509 evaluates whether there is an abnormality in the pronation / extroversion foot based on the post-walking data 527.
  • FIG. 20 is a flowchart showing a processing example of the pronation / extroversion foot evaluation unit 509.
  • the pronation / extroversion foot evaluation unit 509 refers to the COP bending angle from the post-walking processing data 527 (step S221).
  • the pronation / extroversion foot evaluation unit 509 determines whether or not the COP flexion angle is smaller than 155 (step S222), and if it is determined that the COP flexion angle is smaller than 155 (YES in step S222), It is evaluated that there is a possibility of an outer leg (step S223).
  • the pronation foot / exterior foot evaluation unit 509 determines whether or not the COP bending angle is larger than 175 (step S224). If it is determined that the COP bending angle is greater than 175 (YES in step S224), it is evaluated that there is a possibility of a pronation foot (step S225). When determining that the COP flexion angle is not larger than 175 (NO in step S224), the pronation / extroversion foot evaluation unit 509 evaluates normal (step S226).
  • the multi-step balance evaluation unit 510 evaluates the multi-step balance based on the post-walking processing data 527.
  • FIG. 21 is a flowchart showing a processing example of the multi-step balance evaluation unit 510.
  • the multi-step balance evaluation unit 510 refers to the intermediate walking period COP_X coordinate range from the post-walking data 527 (step S231), and determines whether the intermediate walking period COP_X coordinate range is greater than 1 (step S232).
  • the multi-step balance evaluation unit 510 determines that the mid-walk COP_X coordinate range is larger than 1 (YES in step S232)
  • the multi-step balance evaluation unit 510 evaluates that the variation in the multi-step balance when walking is large (step S233). Further, when determining that the middle walking COP_X coordinate range is not larger than 1 (NO in step S232), the multiple-step balance evaluation unit 510 evaluates that the variation in the multiple-step balance when walking is small (step S234).
  • FIG. 22 and FIG. 23 are diagrams showing examples of COP variation, FIG. 22 shows an example where COP variation at a plurality of steps is small, and FIG. 23 shows an example where COP variation at a plurality of steps is large.
  • the stance time evaluation unit 511 evaluates the stance time based on the post-walking processing data 527.
  • FIG. 24 is a flowchart showing a processing example of the stance time evaluation unit 511.
  • the stance time evaluation unit 511 refers to the stance time as a time parameter from the post-walking data 527 (step S241) and compares it with the normal value of the stance time (eg, 600 to 900 ms). Evaluation such as shorter or longer is performed (step S242).
  • the both-leg support ratio evaluation unit 512 evaluates the both-leg support ratio based on the post-walking processing data 527.
  • FIG. 25 is a flowchart showing a processing example of the both-leg support ratio evaluation unit 512.
  • the both-leg support ratio evaluation unit 512 refers to the both-leg support ratio and the single-leg support ratio as time parameters from the post-walking data 527 (step S251), and each normal value (for example, about the both-leg support ratio). 20% to 40% and the single leg support ratio is 60% to 80%), and the evaluation is normal or less or more (step S252).
  • the left / right difference evaluation unit 513 evaluates the left / right difference in the static standing state based on the post-static standing process data 528.
  • FIG. 26 is a flowchart showing a processing example of the left / right difference evaluation unit 513.
  • the left / right difference evaluation unit 513 refers to the load ratios of the left foot 1st to 7th from the post-static standing process data 528 (step S311) and calculates the total left foot load ratio (Sum_Left) (step S312). .
  • the left / right difference evaluation unit 513 refers to the load ratios of the right foot No. 1 to No. 7 from the post-static standing process data 528 (Step S313), and calculates the right foot load ratio sum (Sum_Right) (Step S314).
  • the left / right difference evaluation unit 513 determines a value obtained by subtracting the right foot load ratio sum (Sum_Right) from the left foot load ratio sum (Sum_Left) (step S315). If it is less than ⁇ 20, it is evaluated that it is a right foot load (step S317), and if it is greater than ⁇ 20 and less than 20, it is evaluated as normal (step S318).
  • the foot arch evaluation unit 514 evaluates the formation state of the foot arch in the static standing state based on the post-static standing process data 528, and evaluates whether or not a flat foot is likely to be generated.
  • FIG. 27 is a flowchart showing a processing example of the foot arch evaluation unit 514.
  • the foot arch evaluation unit 514 refers to the left foot 2 sensor: middle foot 1 load ratio and the left foot 6 sensor: middle foot 2 load ratio from the post-static standing processing data 528 (step S321). ).
  • the foot arch evaluation unit 514 compares the load ratios of the left foot second sensor and the sixth sensor (step S322).
  • the foot arch evaluation unit 514 evaluates that the left foot is normal (step S323), and the load ratio of the second sensor is equal to or less than the load ratio of the sixth sensor. If it is, it is evaluated that the foot arch formation of the left foot is insufficient (step S324).
  • the foot arch evaluation unit 514 refers to the right foot 2 sensor: middle foot 1 load ratio and the right foot 6 sensor: middle foot 2 load ratio from the post-static standing process data 528 (steps S325 and S326). ).
  • the foot arch evaluation unit 514 compares the right foot second sensor and the sixth sensor load ratio (steps S327 and S328).
  • the foot arch evaluation unit 514 determines that the left foot is normal and the right foot arch is formed when the load ratio of the 2nd sensor is equal to or less than the load ratio of the 6th sensor in the comparison (step S327) after the evaluation that the left foot is normal (step S323). Evaluation is insufficient (step S329), and in this case, it is determined that the right foot may be a flat foot (step S330). If the load ratio of the second sensor is larger than the load ratio of the sixth sensor, it is evaluated that both feet are normal (step S331).
  • the foot arch evaluation unit 514 determines that the left foot arch formation is insufficient (step S324) and the comparison is made after the load ratio of the second sensor is larger than the load ratio of the sixth sensor in the comparison (step S328). It is evaluated that the right foot is normal with insufficient arch formation (step S332), and in this case, it is determined that the left foot may be a flat foot (step S333). Further, when the load ratio of the second sensor is equal to or less than the load ratio of the sixth sensor, it is evaluated that the arch of both feet is insufficiently formed (step S334), and it is determined that there is a possibility of both feet flat feet (step S335). ).
  • FIG. 28 is a flowchart showing a processing example of the toe contact evaluation unit 517.
  • the toe contact evaluation unit 517 refers to the left foot 4 sensor: forefoot 2 maximum pressure value average and the right foot 4 sensor: forefoot 2 maximum pressure value average from the post-walking data 527 (step S341). ).
  • the mother's ground contact evaluation unit 517 evaluates the ground contact of the mother's body based on the total value of the pressure values indicated by both sensors (step S342). For example, the evaluation is performed by comparing the total value with a predetermined value set in advance.
  • the predetermined value is 5N (Newton).
  • the predetermined value is a value that can be set, for example, a value determined by weight or the like.
  • the predetermined value is “5N”
  • the weight is about “40 kg to 50 kg”.
  • the predetermined value may be “50 kPa”. That is, the evaluation may be performed with either force or pressure.
  • the toe ground contact evaluation unit 517 evaluates that the toe contact is normal (step S343).
  • the toe ground contact evaluation unit 517 evaluates that the toe touch is weak and that the toe is slightly floating (step S344). ).
  • the toe ground contact evaluation unit 517 evaluates that the toe is not touched and is a floating finger (step S345). ).
  • the mother ground contact evaluation unit 517 outputs the evaluation result of the mother ground contact.
  • the comprehensive evaluation unit 515 includes a forefoot local load evaluation unit 508, a pronation foot / extraction foot evaluation unit 509, a multi-step balance evaluation unit 510, a stance time evaluation unit 511, a both-leg support ratio evaluation unit 512, and a left-right difference evaluation unit 513.
  • the overall evaluation for each user is performed with reference to the evaluation result by the foot arch evaluation unit 514, the evaluation result by the toe contact evaluation unit 517, and the life log data 524.
  • FIG. 29 is a flowchart showing a processing example of the comprehensive evaluation unit 515.
  • the comprehensive evaluation unit 515 calculates the body balance degree based on the evaluation result of the multi-step balance evaluation unit 510 and the evaluation result of the left / right difference evaluation unit 513 (step S41). Specifically, by adding, multiplying, standardizing, etc. each evaluation result (the one that has not been digitized is digitized), it is finally scored to a value of 1 to 10, for example, to improve the body balance degree. calculate.
  • the comprehensive evaluation unit 515 calculates a walking rhythm (degree) based on the evaluation result of the stance time evaluation unit 511 and the evaluation result of the both leg support ratio evaluation unit 512 (step S42). Specifically, by adding, multiplying, standardizing, etc. each evaluation result (unquantized ones are digitized), the score is finally converted to a value of 1 to 10, for example, to change the walking rhythm calculate.
  • the comprehensive evaluation unit 515 determines the beauty foot based on the evaluation result of the forefoot local load evaluation unit 508, the evaluation result of the pronation foot / extroversion foot evaluation unit 509, and the evaluation result of the foot arch evaluation unit 514.
  • the degree is calculated (step S43). Specifically, by adding, multiplying, standardizing, etc. each evaluation result (the one that has not been digitized is digitized), it is finally scored to a value of 1 to 10, for example, to improve the leg calculate.
  • the degree of similarity may be taken into account when calculating the degree of beauty, such as the degree of similarity compared to a normal healthy foot or whether an anatomically abnormal load is applied.
  • the degree of beauty is a value that increases when the foot is in a healthy state.
  • the degree of beauty foot is calculated by using at least one of the eye / fish eye evaluation result, the pronation / extraction foot evaluation result, the foot arch evaluation result, and the evaluation result of the toe contact with the heel. .
  • the evaluation result may be weighted. That is, the evaluation result of the toe contact may be added to the calculation of the degree of beauty.
  • the foot arch evaluation result has a higher weight as a value indicating the health condition than the other evaluation results. Therefore, the leg degree may be calculated only from the foot arch evaluation result, or may be calculated by increasing the weight of the foot arch evaluation result. With such a calculation method, it is possible to accurately calculate the degree of beauty.
  • the comprehensive evaluation unit 515 refers to the life log data 524 and calculates an activity level (step S44). Specifically, the degree of activity is calculated by finally scoring to a value of 1 to 10, for example, by adding, multiplying, normalizing, etc. the moving distance, the number of steps, and the average walking speed within the most recent predetermined period.
  • the calculated comprehensive evaluation results can be displayed (visualized) by plotting on a graph (radar chart) as shown in FIG.
  • the comprehensive analysis unit 516 performs a comprehensive analysis of walking motion / daily life activity data based on the data obtained by the above-described processing and other additional data. Additional data includes user data 522, shoe data 523, and life log data 524. “Daily schedule”, “Destination”, etc. are used by manually inputting the user and acquiring information recorded in an Internet service having an arbitrary schedule function or calendar function used by the user. “Movement distance”, “number of steps”, “average walking speed”, “most frequent position information (GPS)” and the like are acquired from the information terminal 3 or any wearable information terminal used by the user, and are calculated from the acquired information and recorded.
  • GPS most frequent position information
  • “Shoemaker model number” and the like are obtained by manual input by a user, reading of a shoe product barcode, or the like.
  • information such as “Shoe rubbing presence / absence”, “Shoe rubbing site”, “Wheel presence / absence”, “Hail location”, “Pain presence / absence”, “Pain location”, “Pressure presence / absence”, “Pressure location”, etc. Entered.
  • the comprehensive analysis unit 516 has a foot state that is similar to the current foot state of the user of the system, and how the other person who has a close amount of action is later than the similar time point. It is possible to determine whether or not the foot state has been changed, and to predict and predict the future foot state.
  • the process up to the comprehensive evaluation by the comprehensive evaluation unit 515 is performed on the accumulated post-walking data 527 of all the users, and the comprehensive analysis unit 516 can divide groups of similar tendencies by performing clustering.
  • the comprehensive analysis unit 516 can show the result as a ratio to the total number of groups to which the user belongs (small or large) and a distance to a comprehensive evaluation index distribution of a good group.
  • a radar chart as shown in FIG. 30 can be used for this feedback.
  • the comprehensive analysis unit 516 presents shoes candidates suitable for the user together with feedback of other analysis results or in response to individual requests.
  • FIG. 31 is a flowchart showing an example of recommended shoe processing.
  • the comprehensive analysis unit 516 extracts other users who are similar in a walking / still standing state to the target user (step S51). Similarity in this case may be obtained by extracting information on “foot length”, “foot width”, “foot height”, and “foot circumference” from the user data 522, and looking at the similarity of the outer shape of the foot. Similarity of each parameter may be seen from post-position processing data 528.
  • the comprehensive analysis unit 516 narrows down to other users who have similar evaluation results from the evaluation unit 507 or the comprehensive evaluation unit 515 (step S52).
  • the comprehensive analysis unit 516 narrows down the narrowed-down users to those who have not been evaluated for failures after the time when they are regarded as similar (step S53).
  • the comprehensive analysis unit 516 acquires the shoe data 523 that has been used since the similar time of other narrowed-down users and presents it as a recommended shoe selection candidate (step S54).
  • the comprehensive analysis unit 516 acquires information such as the sex and age / generation of the target user and other users from the user data 522, and further acquires various types of information indicating behavior patterns from the life log data 524. You may make it narrow down further to other similar users.
  • auxiliary devices such as an insole, a toe correction pad, and a supporter for hallux valgus.
  • the comprehensive analysis unit 516 predicts foot abnormalities due to the use of shoes in the following procedure.
  • step 3 From the post-processing data specified in step 1, data having a walking function similar to that of the user of this system is selected. The processing contents will be described later.
  • the relevant user has information about the feet and shoes when using similar shoes (specifically, “formation of wrinkles”, “presence / absence of shoe rubs”, “shoes” ”Pressure” and “pain” information), it is determined that there is a possibility that the foot abnormalities may occur or not when the shoes selected by the user of the system are used.
  • the possibility of “presence / absence of wrinkle formation”, “presence / absence of shoe rub”, “presence / absence of pressure”, and “presence / absence of pain” can be predicted, and it is possible to predict over time from the start of wearing to the occurrence of occurrence. Become.
  • FIG. 32 is a flowchart showing a processing example of target data determination similar to the walking function in the above-described processing “3.”.
  • the comprehensive analysis unit 516 uses the post-walking processing data 527 for the user A and the other user group B of this system to calculate the maximum pressure value average of the sensors of the feet 3, 4, 5, and 7 and the COP flexion angle. Are extracted and referenced (step S61).
  • the comprehensive analysis unit 516 specifies the bed leaving area from the average maximum pressure value of the sensors of the three feet 3, 4, 5, and 7 (step S62).
  • the comprehensive analysis unit 516 extracts a target in which the area at the time of bed leaving coincides with A from the group B (step S63). If the areas at the time of getting out do not match, the process ends.
  • the comprehensive analysis unit 516 sets walking type A when the COP bending angle is larger than 175, walking type B when the COP bending angle is smaller than 155, and walking type C otherwise (step S64).
  • the comprehensive analysis unit 516 extracts a target in which the walking type (A to C) matches A from the group B (step S65), and determines a comparison target for A (step S66). If the walking type does not match, the process ends. In this case, users having data belonging to the same walking type can be regarded as having a walking function close to each other.
  • ⁇ Summary> it is possible to capture temporal changes in foot / walking data during daily life of a user, collect data of a plurality of users uniformly, and perform appropriate analysis / evaluation. Done. More specifically, ⁇ Evaluation / prediction is possible not only by individual comparison of foot condition and walking function, but also by comparison between individuals with other data. -Optimization of walking (optimization of foot pain, etc.) by predicting signs of disease or disability can be realized. -It leads to user's own health management and shoe selection in daily life. ⁇ All age groups from children to the elderly are targeted, so it is possible to analyze aged data as a record of child development and growth.

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Abstract

In the present invention, a computer executes processes for: acquiring, from at least one sensor disposed on the insole of shoes used by a plurality of users, at least foot sole pressure data for a prescribed amount of time when walking and when stationary; and analyzing the acquired data to acquire and accumulate, for each user, at least a foot sole pressure parameter, foot pressure center parameter and time parameter when walking, and at least a foot sole pressure parameter and foot pressure center parameter when stationary.

Description

歩行・足部評価方法、歩行・足部評価プログラムおよび歩行・足部評価装置Walking / foot evaluation method, walking / foot evaluation program, and walking / foot evaluation device
　本発明は、歩行・足部評価方法、歩行・足部評価プログラムおよび歩行・足部評価装置に関する。 The present invention relates to a walking / foot evaluation method, a walking / foot evaluation program, and a walking / foot evaluation device.
　関連分野において、ヒトの足底圧を検知するための装置の開発に関する発明がなされている（特許文献１、２）。また、静止立位状態において足部形状を計測し、足部アーチ高のデータから靴選択支援を行う技術（特許文献３）や、フィッティングポイントと盛り上げ高さを変えた標本中敷により無理に歩行バランスを崩した歩行運動データと、利用者から計測した加速度とを比較して、フィッティングポイントと盛り上げ高さを決定する技術（特許文献４）も提案されている。 In related fields, inventions relating to the development of devices for detecting human plantar pressure have been made (Patent Documents 1 and 2). In addition, the foot shape is measured in a standing position, and the shoe is supported by a technique that supports shoe selection based on foot arch height data (Patent Document 3) and a sample insole with a different fitting point and raised height. There has also been proposed a technique (Patent Document 4) for determining a fitting point and a raised height by comparing the walking motion data out of balance with the acceleration measured by the user.
　これまでに本発明者らは、足底圧を評価するための計測装置を開発し、その有用性や得られたデータの分析結果について報告した（非特許文献１～３）。すなわち、足底圧を評価するための計測装置の開発およびその有効性の検証（非特許文献１）、開発デバイスを用いた足部アーチ構造と足底圧の関連に関する調査（非特許文献２）、足底部圧力値特徴と高齢者の転倒歴との関連の調査（非特許文献３）を進め、足部の状態を日常生活の中で評価可能な仕組みの提案と、得られるデータの特徴量について評価を行ってきた。 So far, the present inventors have developed a measuring device for evaluating plantar pressure, and reported on its usefulness and analysis results of the obtained data (Non-Patent Documents 1 to 3). That is, development of a measuring device for evaluating plantar pressure and verification of its effectiveness (Non-Patent Document 1), investigation on the relationship between the foot arch structure and plantar pressure using the developed device (Non-Patent Document 2) , Research on the relationship between plantar pressure value characteristics and the fall history of the elderly (Non-Patent Document 3), proposal of a mechanism that can evaluate the state of the foot in daily life, and the feature value of the data obtained Have been evaluated.
特表２０１１－５０５０１５号公報Special table 2011-505015 特開２００９－２５４８１１号公報JP 2009-254811 A Ｗ０２００５／００６９０５W02005 / 006905 特開２００７－１４４１４７号公報JP 2007-144147 A
　上述した従来の技術は、個別の対象者からデータを取得することは可能であったが、複数のユーザのデータを統一的に収集することは考慮されておらず、適切な分析・評価を行う上で十分ではなかった。また、従来技術は静止した状態の計測データや試験的に設定された環境における歩行状態の計測データを用いており、いずれも、日常生活における歩行動作からデータを取得して分析・評価を行うことは実現されていない。 Although the above-mentioned conventional technology was able to acquire data from individual subjects, it does not consider collecting data of multiple users in a unified manner, and performs appropriate analysis and evaluation It was not enough on. In addition, the conventional technology uses measurement data in a stationary state or measurement data of a walking state in an experimentally set environment, both of which acquire data from walking movements in daily life and perform analysis / evaluation Is not realized.
　本発明は上記の従来の問題点に鑑み提案されたものであり、その目的とするところは、ユーザの日常の歩行状態をデータとして把捉し、さらに複数のユーザのデータが統一的に収集され、適切な分析・評価が行われるようにすることにある。 The present invention has been proposed in view of the above-described conventional problems, the purpose of which is to grasp the daily walking state of the user as data, and further collect data of a plurality of users in a unified manner, It is to ensure that appropriate analysis and evaluation is performed.
　上記の課題を解決するため、本発明にあっては、複数のユーザが使用する靴のインソールに設けられた１以上のセンサから歩行時および静止立位時における所定時間の少なくとも足底圧のデータを取得し、取得されたデータを解析して、ユーザ毎の、歩行時における少なくとも足底圧パラメータ、足圧中心パラメータおよび時間パラメータと、静止立位時における少なくとも足底圧パラメータおよび足圧中心パラメータとを取得して蓄積する、処理をコンピュータが実行する。 In order to solve the above problems, in the present invention, at least plantar pressure data for a predetermined time during walking and standing from one or more sensors provided on an insole of a shoe used by a plurality of users. And analyzing the acquired data, and for each user, at least the plantar pressure parameter, the foot pressure center parameter and the time parameter during walking, and at least the plantar pressure parameter and the foot pressure center parameter when standing still The computer executes the process of acquiring and accumulating.
　本発明にあっては、ユーザの日常の歩行動作から得られる情報から、複数のユーザのデータが統一的に収集され、適切な分析・評価が行われる。 In the present invention, data of a plurality of users are uniformly collected from information obtained from daily walking motions of the user, and appropriate analysis / evaluation is performed.
本発明の一実施形態にかかるシステムの構成例を示す図である。It is a figure which shows the structural example of the system concerning one Embodiment of this invention. 各種データの構造例を示す図（その１）である。It is a figure (the 1) which shows the example of a structure of various data. 各種データの構造例を示す図（その２）である。It is FIG. (2) which shows the structural example of various data. 各種データの構造例を示す図（その３）である。It is FIG. (3) which shows the structural example of various data. 各種データの構造例を示す図（その４）である。It is FIG. (4) which shows the structural example of various data. 各種データの構造例を示す図（その５）である。It is FIG. (5) which shows the structural example of various data. 各種データの構造例を示す図（その６）である。It is FIG. (6) which shows the structural example of various data. 各種データの構造例を示す図（その７）である。It is FIG. (7) which shows the example of a structure of various data. センサ位置の例を示す図である。It is a figure which shows the example of a sensor position. 計測デバイス、情報端末、サーバ装置および管理端末の情報処理にかかるハードウェア構成例を示す図である。It is a figure which shows the hardware structural example concerning the information processing of a measurement device, an information terminal, a server apparatus, and a management terminal. 立脚期データ解析部の処理例を示すフローチャートである。It is a flowchart which shows the process example of a stance phase data analysis part. 波形データの例を示す図（その１）である。It is a figure (the 1) which shows the example of waveform data. 波形データの例を示す図（その２）である。It is a figure (the 2) which shows the example of waveform data. 歩行時の足底圧パラメータの抽出の例を示す図である。It is a figure which shows the example of extraction of the plantar pressure parameter at the time of a walk. 歩行時のＣＯＰパラメータの抽出の例を示す図である。It is a figure which shows the example of extraction of the COP parameter at the time of a walk. 歩行時の時間パラメータの抽出の例を示す図である。It is a figure which shows the example of extraction of the time parameter at the time of a walk. 静止立位データ解析部の処理例を示すフローチャートである。It is a flowchart which shows the process example of a stationary standing data analysis part. 静止立位時の足底圧パラメータとＣＯＰパラメータの抽出の例を示す図である。It is a figure which shows the example of extraction of the sole pressure parameter and COP parameter at the time of a stationary standing. 前足部局所荷重評価部の処理例を示すフローチャートである。It is a flowchart which shows the process example of a forefoot local load evaluation part. 回内足・回外足評価部の処理例を示すフローチャートである。It is a flowchart which shows the example of a process of a pronation foot / extroversion foot evaluation part. 複数歩バランス評価部の処理例を示すフローチャートである。It is a flowchart which shows the process example of a multiple step balance evaluation part. ＣＯＰのばらつきの例を示す図（その１）である。It is a figure (the 1) which shows the example of the dispersion | variation in COP. ＣＯＰのばらつきの例を示す図（その２）である。FIG. 6 is a second diagram illustrating an example of COP variation. 立脚時間評価部の処理例を示すフローチャートである。It is a flowchart which shows the process example of a stance time evaluation part. 両脚支持割合評価部の処理例を示すフローチャートである。It is a flowchart which shows the process example of a both leg support ratio evaluation part. 左右差評価部の処理例を示すフローチャートである。It is a flowchart which shows the process example of a right-and-left difference evaluation part. 足部アーチ評価部の処理例を示すフローチャートである。It is a flowchart which shows the process example of a foot | leg part arch evaluation part. 母趾接地評価部の処理例を示すフローチャートである。It is a flowchart which shows the process example of a mother ground contact evaluation part. 総合評価部の処理例を示すフローチャートである。It is a flowchart which shows the process example of a comprehensive evaluation part. 総合評価結果のグラフ化の例を示す図である。It is a figure which shows the example of graphing of a comprehensive evaluation result. 靴の推奨の処理例を示すフローチャートである。It is a flowchart which shows the process example of recommendation of shoes. 歩行機能の類似する対象データ決定の処理例を示すフローチャートである。It is a flowchart which shows the process example of the object data determination with which a walking function is similar.
　以下、本発明の好適な実施形態につき説明する。 Hereinafter, preferred embodiments of the present invention will be described.
　＜構成＞
　図１は本発明の一実施形態にかかるシステムの構成例を示す図である。図１において、ユーザが使用する靴１（左右）には計測デバイス（靴デバイス）２が設けられており、センサ部２１で検出された信号のデータは通信部２２を介し、Bluetooth（登録商標）、無線ＬＡＮ（Local Area Network）等の無線通信手段によりスマートフォン、タブレット、ＰＣ（Personal Computer）等の情報端末３に送信されるようになっている。計測デバイス２から情報端末３へのデータ送信間隔は、例えば、１０ｍｓ（ミリ秒、１００Ｈｚ）である。センサ部２１は、例えば、インソール（中敷き）型の基材２１１に複数（図示の例では片足に７個）の圧力センサ２１２が設けられている。なお、圧力センサ以外に、剪断力（摩擦力）センサ、加速度センサ、温度センサ、湿度センサ等が設けられるようにしてもよい。更に、インソールには、情報端末３側からの制御により、色が変化する機構（視覚刺激を与える機構）や素材が変形したり硬さが変化したりする機構（触覚刺激を与える機構）が設けられることで、ユーザに対して歩行や足部の状態がフィードバックされるようにしてもよい。また、通信部２２は、ＧＰＳ（Global Positioning System）等による位置データも送信する機能を備えている。位置データは、計測デバイス２からに代え、情報端末３から取得されるようにしてもよい。 <Configuration>
FIG. 1 is a diagram showing a configuration example of a system according to an embodiment of the present invention. In FIG. 1, a shoe 1 (left and right) used by a user is provided with a measuring device (shoe device) 2, and signal data detected by a sensor unit 21 is transmitted via Bluetooth (registered trademark) via a communication unit 22. The information is transmitted to an information terminal 3 such as a smartphone, a tablet, or a PC (Personal Computer) by wireless communication means such as a wireless local area network (LAN). The data transmission interval from the measuring device 2 to the information terminal 3 is, for example, 10 ms (millisecond, 100 Hz). The sensor unit 21 includes, for example, a plurality of (seven in the illustrated example) pressure sensors 212 on an insole (insole) type base material 211. In addition to the pressure sensor, a shearing force (frictional force) sensor, an acceleration sensor, a temperature sensor, a humidity sensor, and the like may be provided. Furthermore, the insole is provided with a mechanism for changing color (mechanism for applying visual stimulation) or a mechanism for changing material or changing hardness (mechanism for applying tactile stimulation) by control from the information terminal 3 side. By doing so, the user may be fed back with respect to the state of walking or foot. The communication unit 22 also has a function of transmitting position data by GPS (Global Positioning System) or the like. The position data may be acquired from the information terminal 3 instead of the measurement device 2.
　情報端末３は、計測デバイス２から受信して一時的に蓄積していたデータを、所定時間間隔（例えば、１０ｓ）毎に、移動無線ネットワークやインターネット等のネットワーク４を介してサーバ装置５に送信する機能を有している。また、情報端末３は、サーバ装置５からユーザの歩行や足部の状態の情報を取得して画面表示を行い、ユーザに対して歩行や足部の状態のフィードバックや靴選択の支援等を行う機能も有している。なお、計測デバイス２から情報端末３を経由してサーバ装置５にデータが送信される場合についての説明であったが、環境によっては、計測デバイス２からサーバ装置５にデータが直接に送信されるものとしてもよい。この場合、情報端末３は計測デバイス２の操作やユーザへのフィードバック等に用いられる。 The information terminal 3 transmits the data received from the measuring device 2 and temporarily accumulated to the server device 5 via a network 4 such as a mobile radio network or the Internet at predetermined time intervals (for example, 10 s). It has a function to do. In addition, the information terminal 3 acquires information on the user's walking and foot state from the server device 5 and displays the screen, and provides the user with feedback on walking and foot state, shoe selection support, and the like. It also has a function. In addition, although it was description about the case where data are transmitted to the server apparatus 5 via the information terminal 3 from the measurement device 2, data are directly transmitted from the measurement device 2 to the server apparatus 5 depending on an environment. It may be a thing. In this case, the information terminal 3 is used for the operation of the measuring device 2 and feedback to the user.
　サーバ装置５は、処理機能を実現する部分として、基本データ入力部５０１と計測データ受信部５０２とデータ解析部５０３とライフログ書込部５０６と評価部５０７と総合評価部５１５と総合分析部５１６とを備えている。また、処理に際して用いられるデータを蓄積するデータベース５２１が設けられている。データベース５２１はサーバ装置５とは別に保持・管理されるようにしてもよい。 The server device 5 includes a basic data input unit 501, a measurement data receiving unit 502, a data analysis unit 503, a life log writing unit 506, an evaluation unit 507, a comprehensive evaluation unit 515, and a comprehensive analysis unit 516 as parts for realizing processing functions. And. Further, a database 521 for accumulating data used for processing is provided. The database 521 may be held and managed separately from the server device 5.
　データベース５２１には、ユーザデータ５２２と靴データ５２３とライフログデータ５２４と位置データ５２５と計測データ５２６と歩行処理後データ５２７と静止立位処理後データ５２８とが保持されている。ユーザデータ５２２と靴データ５２３とは、ユーザや靴等の基本的なデータである。ライフログデータ５２４は、ユーザの行動（予定を含む）を示すデータである。位置データ５２５は、計測デバイス２または情報端末３から、計測データ５２６は計測デバイス２から、それぞれ取得されるデータである。歩行処理後データ５２７と静止立位処理後データ５２８は、計測データ５２６から解析により得られるデータである。 The database 521 holds user data 522, shoe data 523, life log data 524, position data 525, measurement data 526, post-walking processing data 527, and stationary post-processing data 528. User data 522 and shoe data 523 are basic data such as users and shoes. The life log data 524 is data indicating user behavior (including a schedule). The position data 525 is data acquired from the measurement device 2 or the information terminal 3, and the measurement data 526 is data acquired from the measurement device 2. The post-walking processing data 527 and the stationary standing processing post-processing data 528 are data obtained by analysis from the measurement data 526.
　ユーザデータ５２２は、図２に例示されるように、「ユーザＩＤ」「名前」「靴ＩＤ」「性別」「生年月日」「身長」「体重」「靴サイズ」「足長」「足幅」「足高」「足囲」「登録日」「更新日」等の項目を有している。靴データ５２３は、図３に例示されるように、「靴ＩＤ」「ユーザＩＤ」「購入日」「使用開始日」「靴店舗ＩＤ」「靴メーカー型番」「靴タイプ」「テーマ」「靴擦れ有無」「靴擦れ部位」「胼胝有無」「胼胝部位」「痛み有無」「痛み部位」「圧迫有無」「圧迫部位」「登録日」「更新日」等の項目を有している。「靴擦れ有無」「靴擦れ部位」「胼胝有無」「胼胝部位」「痛み有無」「痛み部位」「圧迫有無」「圧迫部位」等は、例えば、ユーザから入力される。 As illustrated in FIG. 2, the user data 522 includes “user ID”, “name”, “shoe ID”, “sex”, “birth date”, “height”, “weight”, “shoe size”, “foot length”, “foot width”. ”,“ Foot height ”,“ foot circumference ”,“ registration date ”, and“ update date ”. As illustrated in FIG. 3, the shoe data 523 includes “shoe ID”, “user ID”, “purchase date”, “use start date”, “shoe store ID”, “shoe manufacturer model number”, “shoe type”, “theme”, “shoe rub”. It has items such as “presence / absence”, “shoe rubbing part”, “heel presence / absence”, “heel part”, “pain presence / absence”, “pain part”, “pressure presence / absence”, “pressure part”, “registration date”, “update date”. “Shoe rub presence / absence”, “shoe rub site”, “heel presence / absence”, “heel region”, “pain presence / absence”, “pain region”, “pressure presence / absence”, “pressure region”, etc. are input from the user, for example.
　ライフログデータ５２４は、図４に例示されるように、「ログＩＤ」「年月日時刻」「ユーザＩＤ」「１日の予定」「目的地」「移動距離」「歩数」「平均歩行速度」「最多位置情報（ＧＰＳ）」「登録日」「更新日」等の項目を有している。 As illustrated in FIG. 4, the life log data 524 includes “log ID”, “year / month / day / time”, “user ID”, “plan for one day”, “destination”, “movement distance”, “step count”, and “average walking speed”. ”,“ Most position information (GPS) ”,“ registration date ”,“ update date ”, and the like.
　位置データ５２５は、図５に例示されるように、「年月日時刻」「ユーザＩＤ」「位置情報（ＧＰＳ）」等の項目を有している。 The position data 525 has items such as “year / month / day / time”, “user ID”, and “position information (GPS)” as illustrated in FIG.
　計測データ５２６は、図６に例示されるように、「年月日時刻」「ユーザＩＤ」「左足１番センサ：後足部（踵）圧力値」「左足２番センサ：中足部１）圧力値」「左足３番センサ：前足部１）圧力値」「左足４番センサ：前足部２圧力値」「左足５番センサ：前足部３圧力値」「左足６番センサ：中足部２圧力値」「左足７番センサ：前足部４圧力値」「右足１番センサ：後足部（踵）圧力値」「右足２番センサ：中足部１圧力値」「右足３番センサ：前足部１圧力値」「右足４番センサ：前足部２圧力値」「右足５番センサ：前足部３圧力値」「右足６番センサ：中足部２圧力値」「右足７番センサ：前足部４圧力値」「両足足圧中心ＣＯＰ_Ｘ座標値」「両足足圧中心ＣＯＰ_Ｙ座標値」「左足足圧中心ＣＯＰ_Ｘ座標値」「左足足圧中心ＣＯＰ_Ｙ座標値」「右足足圧中心ＣＯＰ_Ｘ座標値」「右足足圧中心ＣＯＰ_Ｙ座標値」等の項目を有している。センサの番号は、図９に示されるものとしている。波形データは、サンプリング周期（例えば、１０ｍｓ）で情報端末３において蓄積される時間（例えば、１０ｓ）分の圧力値の連続データである。ＣＯＰは足圧中心（Center of Pressure）である。「左足１番センサ：後足部（踵）圧力値」等は圧力値の波形データを示している。「両足足圧中心ＣＯＰ_Ｘ座標値」等は座標値の軌跡を示している。 As illustrated in FIG. 6, the measurement data 526 includes “year / month / day / time”, “user ID”, “left foot 1 sensor: hind foot (heel) pressure value”, “left foot 2 sensor: middle foot 1). “Pressure value” “Left foot No. 3 sensor: Forefoot 1) Pressure value” “Left foot No. 4 sensor: Forefoot 2 pressure value” “Left foot No. 5 sensor: Forefoot 3 pressure value” “Left foot No. 6 sensor: Middle foot 2” “Pressure value” “Left foot 7 sensor: Forefoot 4 pressure value” “Right foot 1 sensor: Rear foot (後) pressure value” “Right foot 2 sensor: Middle foot 1 pressure value” “Right foot 3 sensor: Front foot "1 foot pressure value" "right foot 4 sensor: forefoot 2 pressure value" "right foot 5 sensor: forefoot 3 pressure value" "right foot 6 sensor: middle foot 2 pressure value" "right foot 7 sensor: forefoot 4 pressure value "" Both foot pressure center COP_X coordinate value "" Both foot pressure center COP_Y coordinate value "" Left foot foot pressure center COP_X coordinate value "" Left foot foot pressure middle " COP_Y coordinate value "," right foot pressure center COP_X coordinate value "," has an item in the right foot pressure center COP_Y coordinate value ", and the like. The sensor numbers are shown in FIG. The waveform data is continuous data of pressure values for a time (for example, 10 s) accumulated in the information terminal 3 in a sampling period (for example, 10 ms). COP is the center of foot pressure. “Left foot first sensor: rear foot (足) pressure value” and the like indicate waveform data of the pressure value. “Both foot pressure center COP_X coordinate value” or the like indicates a locus of coordinate values.
　歩行処理後データ５２７は、図７に例示されるように、「年月日時刻」「ユーザＩＤ」「歩数」「左足平均立脚時間」「右足平均立脚時間」「両脚支持割合」「左足単脚支持割合」「右足単脚支持割合」「左足１番センサ：後足部（踵）最大圧力値平均」「左足２番センサ：中足部１最大圧力値平均」「左足３番センサ：前足部１最大圧力値平均」「左足４番センサ：前足部２最大圧力値平均」「左足５番センサ：前足部３最大圧力値平均」「左足６番センサ：中足部２最大圧力値平均」「左足７番センサ：前足部４最大圧力値平均」「右足１番センサ：後足部（踵）最大圧力値平均」「右足２番センサ：中足部１最大圧力値平均」「右足３番センサ：前足部１最大圧力値平均」「右足４番センサ：前足部２最大圧力値平均」「右足５番センサ：前足部３最大圧力値平均」「右足６番センサ：中足部２最大圧力値平均」「右足７番センサ：前足部４最大圧力値平均」「ＣＯＰ開始位置Ｘ座標」「ＣＯＰ開始位置Ｙ座標」「ＣＯＰ終了位置Ｘ座標」「ＣＯＰ終了位置Ｙ座標」「左足ＣＯＰ屈曲角」「右足ＣＯＰ屈曲角」「左足歩行中期ＣＯＰ_Ｘ座標範囲」「右足歩行中期ＣＯＰ_Ｘ座標範囲」等の項目を有している。「左足１番センサ：後足部（踵）最大圧力値平均」等は足底圧パラメータである。「左足ＣＯＰ屈曲角」「左足歩行中期ＣＯＰ_Ｘ座標範囲」等はＣＯＰパラメータである。「左足平均立脚時間」「右足平均立脚時間」「両脚支持割合」「左足単脚支持割合」「右足単脚支持割合」は時間パラメータである。 As illustrated in FIG. 7, the post-walking data 527 includes “year / month / day time”, “user ID”, “number of steps”, “left leg average stance time”, “right leg average stance time”, “both leg support ratio”, “left leg monopod”. "Support ratio" "Right foot single leg support ratio" "Left foot 1 sensor: Rear foot (踵) maximum pressure value average" "Left foot 2 sensor: Middle foot 1 maximum pressure value average" "Left foot 3 sensor: Front foot “1st maximum pressure value average” “Left foot # 4 sensor: front foot 2 maximum pressure value average” “Left foot 5 sensor: front foot 3 maximum pressure value average” “Left foot 6th sensor: middle foot 2 maximum pressure value average” “ Left foot 7 sensor: Forefoot 4 maximum pressure value average ”Right foot 1 sensor: Rear foot (踵) maximum pressure value average” “Right foot 2 sensor: Middle foot 1 maximum pressure value average” “Right foot 3rd sensor : Forefoot 1 maximum pressure value average ”“ Right foot 4 sensor: Forefoot 2 maximum pressure value average ”“ Right foot 5th center S: Forefoot 3 maximum pressure value average ”“ Right foot 6 sensor: Middle foot 2 maximum pressure value average ”“ Right foot 7 sensor: Forefoot 4 maximum pressure value average ”“ COP start position X coordinate ”“ COP start position ” Items such as “Y coordinate”, “COP end position X coordinate”, “COP end position Y coordinate”, “left foot COP bending angle”, “right foot COP bending angle”, “left foot walking middle period COP_X coordinate range”, and “right foot walking middle period COP_X coordinate range” are included. is doing. “Left foot No. 1 sensor: rear foot (踵) maximum pressure value average” and the like are plantar pressure parameters. “Left foot COP bending angle”, “Left foot walking middle period COP_X coordinate range” and the like are COP parameters. “Left foot average stance time”, “right foot average stance time”, “both leg support ratio”, “left foot single leg support ratio”, and “right foot single leg support ratio” are time parameters.
　静止立位処理後データ５２８は、図８に例示されるように、「年月日時刻」「ユーザＩＤ」「左足１番センサ：後足部（踵）荷重比率」「左足２番センサ：中足部１荷重比率」「左足３番センサ：前足部１荷重比率」「左足４番センサ：前足部２荷重比率」「左足５番センサ：前足部３荷重比率」「左足６番センサ：中足部２荷重比率」「左足７番センサ：前足部４荷重比率」「右足１番センサ：後足部（踵）荷重比率」「右足２番センサ：中足部１荷重比率」「右足３番センサ：前足部１荷重比率」「右足４番センサ：前足部２荷重比率」「右足５番センサ：前足部３荷重比率」「右足６番センサ：中足部２荷重比率」「右足７番センサ：前足部４荷重比率」「ＣＯＰ総軌跡長」「ＣＯＰ矩形面積」等の項目を有している。「左足１番センサ：後足部（踵）荷重比率」等は足底圧パラメータである。「ＣＯＰ総軌跡長」「ＣＯＰ矩形面積」はＣＯＰパラメータである。 As illustrated in FIG. 8, the post-stillation processing data 528 includes “year / month / day / time”, “user ID”, “left foot 1 sensor: rear foot (heel) load ratio”, “left foot 2 sensor: middle”. "1st foot load ratio" "Left foot 3 sensor: front foot 1 load ratio" "Left foot 4 sensor: front foot 2 load ratio" "Left foot 5 sensor: front foot 3 load ratio" "Left foot 6 sensor: middle foot Section 2 load ratio "" Left foot 7 sensor: Forefoot 4 load ratio "" Right foot 1 sensor: Rear foot (heel) load ratio "" Right foot 2 sensor: Middle foot 1 load ratio "" Right foot 3 sensor : "Forefoot 1 load ratio" "Right foot 4 sensor: Forefoot 2 load ratio" "Right foot 5 sensor: Forefoot 3 load ratio" "Right foot 6 sensor: Middle foot 2 load ratio" "Right foot 7 sensor: It has items such as “front foot 4 load ratio”, “COP total trajectory length”, and “COP rectangular area”. “Left foot No. 1 sensor: rear foot (heel) load ratio” and the like are plantar pressure parameters. “COP total trajectory length” and “COP rectangular area” are COP parameters.
　図１に戻り、基本データ入力部５０１は、ユーザ、靴等の基本的なデータの設定を受け付け、データベース５２１のユーザデータ５２２、靴データ５２３にそれぞれに登録する機能を有している。 Referring back to FIG. 1, the basic data input unit 501 has a function of accepting basic data settings such as users and shoes and registering them in the user data 522 and shoe data 523 of the database 521, respectively.
　計測データ受信部５０２は、計測デバイス２から情報端末３を介して送信されるデータを受信し、データベース５２１の位置データ５２５と計測データ５２６に登録する機能を有している。 The measurement data receiving unit 502 has a function of receiving data transmitted from the measurement device 2 via the information terminal 3 and registering it in the position data 525 and the measurement data 526 of the database 521.
　データ解析部５０３は、立脚期データ解析部５０４と静止立位データ解析部５０５とを備え、後続の評価部５０７による評価の基礎となるデータを生成する機能を有している。すなわち、立脚期データ解析部５０４は計測データ５２６を解析して歩行処理後データ５２７を生成し、静止立位データ解析部５０５は計測データ５２６を解析して静止立位処理後データ５２８を生成する。 The data analysis unit 503 includes a stance phase data analysis unit 504 and a stationary standing data analysis unit 505, and has a function of generating data serving as a basis for evaluation by the subsequent evaluation unit 507. That is, the stance phase data analysis unit 504 analyzes the measurement data 526 and generates post-walking data 527, and the stationary standing data analysis unit 505 analyzes the measurement data 526 and generates post-resting processing data 528. .
　ライフログ書込部５０６は、データベース５２１の位置データ５２５とライフログデータ５２４とに記録を行う機能を有している。 The life log writing unit 506 has a function of recording in the position data 525 and the life log data 524 of the database 521.
　評価部５０７は、前足部局所荷重評価部５０８と回内足・回外足評価部５０９と複数歩バランス評価部５１０と立脚時間評価部５１１と両脚支持割合評価部５１２と左右差評価部５１３と足部アーチ評価部５１４と母趾接地評価部５１７とを備え、各種の評価を行い、評価結果を生成する機能を有している。なお、評価部５０７ではその都度に評価が行われることが想定されているが、評価結果がユーザに対応付けられて保存されるようにしてもよい。 The evaluation unit 507 includes a forefoot local load evaluation unit 508, a pronation foot / extraction foot evaluation unit 509, a multi-step balance evaluation unit 510, a stance time evaluation unit 511, a both-leg support ratio evaluation unit 512, and a left-right difference evaluation unit 513. A foot arch evaluation unit 514 and a buckle contact evaluation unit 517 are provided and have a function of performing various evaluations and generating an evaluation result. In addition, although it is assumed that the evaluation unit 507 evaluates each time, the evaluation result may be stored in association with the user.
　総合評価部５１５は、評価部５０７による各種の評価結果とライフログデータ５２４とから、美足度、カラダバランス度、歩き方リズム、活動度を算出し、ユーザの歩行・足部の総合評価を可視化してユーザに対してフィードバックする機能を有している。 The comprehensive evaluation unit 515 calculates a degree of leg, a body balance, a walking rhythm, and an activity from various evaluation results by the evaluation unit 507 and the life log data 524, and performs a comprehensive evaluation of the user's walking / foot. It has a function of visualizing and feeding back to the user.
　総合分析部５１６は、データベース５２１に蓄積されたデータに基づき、時間的な状態変化を加味して総合分析を行い、足部異常の早期発見・予測警告等を実現する機能を有している。例えば、対象ユーザについて現時点で障害等の発生の可能性が評価されていない場合であっても、他のユーザの過去のデータから足部・静止立位の状態や歩行機能が類似し、その他のユーザがその後に障害等の発生の可能性が評価されている場合に、対象ユーザについても将来的に障害等の発生が推定され、その旨がフィードバックされる。また、総合分析部５１６は、ユーザ個人に合った靴選びのアドバイスや歩き方の提案を行う機能も有している。 The comprehensive analysis unit 516 has a function of performing a comprehensive analysis based on data accumulated in the database 521 and taking into account temporal changes, and realizing early detection / prediction warning of foot abnormalities. For example, even if the possibility of occurrence of a disorder or the like has not been evaluated for the target user at this time, the foot / still standing state and walking function are similar from other users' past data, When the possibility of the occurrence of a failure or the like is subsequently evaluated by the user, the occurrence of the failure or the like is also estimated in the future for the target user, and that fact is fed back. The comprehensive analysis unit 516 also has a function of providing advice on selecting shoes suitable for the individual user and suggesting how to walk.
　また、管理者が操作する管理端末６がネットワーク４を介してサーバ装置５に接続可能となっており、管理者はサーバ装置５で管理されるデータの確認やメンテナンスが行えるようになっている。 Also, the management terminal 6 operated by the administrator can be connected to the server device 5 via the network 4 so that the administrator can confirm and maintain data managed by the server device 5.
　図１０は計測デバイス２、情報端末３、サーバ装置５および管理端末６の情報処理にかかるハードウェア構成例を示す図であり、一般的なコンピュータの構成が示されている。図１０において、計測デバイス２等は、バス２０７を介して相互に接続されたＣＰＵ（Central Processing Unit）２０１、ＲＯＭ（Read Only Memory）２０２、ＲＡＭ（Random Access Memory）２０３、ＳＳＤ（Solid State Drive）／ＨＤＤ（Hard Disk Drive）２０４を備えている。また、計測デバイス２等は、接続Ｉ／Ｆ（Interface）２０５、通信Ｉ／Ｆ２０６を備えている。 FIG. 10 is a diagram illustrating a hardware configuration example related to information processing of the measurement device 2, the information terminal 3, the server device 5, and the management terminal 6, and illustrates a general computer configuration. In FIG. 10, a measurement device 2 and the like are a CPU (Central Processing Unit) 201, a ROM (Read Only Memory) 202, a RAM (Random Access Memory) 203, and an SSD (Solid State Drive) that are connected to each other via a bus 207. / HDD (Hard Disk Disk Drive) 204 is provided. The measuring device 2 and the like include a connection I / F (Interface) 205 and a communication I / F 206.
　ＣＰＵ２０１は、ＲＡＭ２０３をワークエリアとしてＲＯＭ２０２またはＳＳＤ／ＨＤＤ２０４等に格納されたプログラムを実行することで、計測デバイス２等の動作を統括的に制御する。図１で説明した計測デバイス２等の機能は、ＣＰＵ２０１において所定のプログラムが実行されることで実現される。プログラムは、記録媒体を経由して取得されるものでもよいし、ネットワークを経由して取得されるものでもよいし、ＲＯＭ組込でもよい。 The CPU 201 performs overall control of the operation of the measurement device 2 and the like by executing a program stored in the ROM 202 or the SSD / HDD 204 or the like using the RAM 203 as a work area. The functions of the measurement device 2 and the like described in FIG. 1 are realized by executing a predetermined program in the CPU 201. The program may be acquired via a recording medium, may be acquired via a network, or may be embedded in a ROM.
　＜立脚期データ解析部５０４による処理＞
　立脚期データ解析部５０４は、立脚歩行時に取得された計測データ５２６を解析し、歩行処理後データ５２７を生成する。すなわち、立脚期データ解析部５０４は、ヒトの歩行動作を表す波形データから立脚期・遊脚期などの歩行イベント（ヒトの歩行動作はある一方の足を見たときにその足が地面に接地した状態の立脚期と地面から離床した状態の遊脚期に分けられる）を自動検知し、ヒトの歩行動作に関連する数値を取得する。 <Processing by the stance phase data analysis unit 504>
The stance phase data analysis unit 504 analyzes the measurement data 526 acquired during stance walking and generates post-walking processing data 527. In other words, the stance phase data analysis unit 504 uses the waveform data representing the human walking motion to perform walking events such as the stance phase and the free leg phase (when one foot of the human walking motion is seen, the foot touches the ground. Automatically detected in the stance phase in the separated state and the free leg phase in the state of getting out of the ground) to obtain numerical values related to human walking motion.
　図１１は立脚期データ解析部５０４の処理例を示すフローチャートである。図１１において、立脚期データ解析部５０４は、計測データ５２６の各センサの圧力値の波形データを取得し（ステップＳ１１１）、波形の立ち上がりから接地時点を検出し、一歩行毎の波形データを切り出す（ステップＳ１１２）。すなわち、全センサの圧力値が最小になる点から次に全センサの圧力値が最小になる点までの状態を１立脚期とするが、当該の立脚期データが正常な歩行状態のデータであることを次の条件で精査し、これに当てはまるデータをパラメータ抽出に用いる１立脚期とする。
（ａ）歩行一定期間の波形データ全体のうち最も高い圧力値（トップ最大値）を示すセンサを特定し、そのセンサからの波形データの複数の最大値がいずれもトップ最大値を基準として８０％以上の値を示すこと
（ｂ）１立脚期の長さが１２００ｍｓ未満であること FIG. 11 is a flowchart illustrating a processing example of the stance phase data analysis unit 504. In FIG. 11, the stance phase data analysis unit 504 acquires waveform data of pressure values of the respective sensors in the measurement data 526 (step S111), detects a ground contact point from the rising edge of the waveform, and cuts out waveform data for each walk. (Step S112). That is, the state from the point where the pressure values of all the sensors become the minimum to the point where the pressure values of all the sensors become the next is set as one stance phase, but the stance phase data is data of a normal walking state. This is scrutinized under the following conditions, and data corresponding to this is defined as one stance phase used for parameter extraction.
(A) A sensor showing the highest pressure value (top maximum value) in the entire waveform data for a fixed period of walking is specified, and a plurality of maximum values of the waveform data from the sensor are all 80% based on the top maximum value. (B) The length of one stance phase is less than 1200 ms.
　図１２は７個のセンサの波形データの例を示しており、複数歩による波形が記録されているが、これらから図１３に示されるような一歩行毎の波形データが切り出される。 FIG. 12 shows an example of the waveform data of seven sensors, and the waveforms of a plurality of steps are recorded. From these, the waveform data for each walk as shown in FIG. 13 is cut out.
　図１１に戻り、次いで、立脚期データ解析部５０４は、各センサの一歩行毎の波形の最大圧力値を検出し、センサ毎の複数歩の最大圧力値合計を歩数で除算することで平均（「左足１番センサ：後足部（踵）最大圧力値平均」等）をとり、足底圧パラメータとして取得する（ステップＳ１１３）。図１４は各センサの波形から最大値（黒丸で図示）が取得された様子を示している。 Returning to FIG. 11, the stance phase data analysis unit 504 then detects the maximum pressure value of the waveform for each walk of each sensor, and divides the total maximum pressure value of a plurality of steps for each sensor by the number of steps. "Left foot No. 1 sensor: hind foot (踵) maximum pressure value average" etc.) is acquired and acquired as a plantar pressure parameter (step S113). FIG. 14 shows a state in which the maximum value (illustrated by a black circle) is acquired from the waveform of each sensor.
　図１１に戻り、次いで、立脚期データ解析部５０４は、計測データ５２６の「左足足圧中心ＣＯＰ_Ｘ座標値」「左足足圧中心ＣＯＰ_Ｙ座標値」「右足足圧中心ＣＯＰ_Ｘ座標値」「右足足圧中心ＣＯＰ_Ｙ座標値」から、ＣＯＰパラメータとして、「ＣＯＰ開始位置Ｘ座標」「ＣＯＰ開始位置Ｙ座標」「ＣＯＰ終了位置Ｘ座標」「ＣＯＰ終了位置Ｙ座標」「左足ＣＯＰ屈曲角」「右足ＣＯＰ屈曲角」「左足歩行中期ＣＯＰ_Ｘ座標範囲」「右足歩行中期ＣＯＰ_Ｘ座標範囲」を取得する（ステップＳ１１４）。図１５は、ＣＯＰ開始位置であるＡ点から、Ｈ点（３番センサと５番センサを結ぶ直線とＣＯＰ軌跡の交点）を通り、ＣＯＰ終了位置であるＢ点を通るＣＯＰ軌跡が示されており、直線ＡＨと直線ＨＢのなす角度がＣＯＰ屈曲角となる。また、歩行方向（足底の長手方向）となるＹ座標方向におけるＣＯＰ開始位置Ｙ座標とＣＯＰ終了位置Ｙ座標の中点（接地と離床の中間）に最も近いＣＯＰ座標の複数歩における側方方向の最大値と最小値の差が歩行中期ＣＯＰ_Ｘ座標範囲となる。 Returning to FIG. 11, the stance phase data analysis unit 504 then includes the “left foot foot pressure center COP_X coordinate value”, “left foot foot pressure center COP_Y coordinate value”, “right foot foot pressure center COP_X coordinate value”, and “right foot foot pressure” in the measurement data 526. From the center COP_Y coordinate value, as COP parameters, “COP start position X coordinate” “COP start position Y coordinate” “COP end position X coordinate” “COP end position Y coordinate” “left foot COP flex angle” “right foot COP flex angle” "" Left-foot walking middle period COP_X coordinate range "" "Right-foot walking middle period COP_X coordinate range" is acquired (step S114). FIG. 15 shows a COP trajectory passing from point A, which is the COP start position, through point H, (the intersection of the straight line connecting the third sensor and the fifth sensor and the COP trajectory) and passing through point B, which is the COP end position. The angle formed by the straight line AH and the straight line HB is the COP bending angle. In addition, the lateral direction in a plurality of steps of the COP coordinate closest to the midpoint of the COP start position Y coordinate and the COP end position Y coordinate (intermediate between ground contact and getting out of bed) in the Y coordinate direction that is the walking direction (longitudinal direction of the sole) The difference between the maximum value and the minimum value is the middle walking COP_X coordinate range.
　図１１に戻り、次いで、立脚期データ解析部５０４は、時間パラメータとして、複数歩の平均である「左足平均立脚時間」「右足平均立脚時間」「両脚支持割合」「左足単脚支持割合」「右足単脚支持割合」を取得する（ステップＳ１１５）。図１６は左右の足の接地状態を示しており、片足の接地から離床までが立脚時間となり、両足が接地している期間が両脚支持期となり、片足のみが接地している期間が単脚支持期となる。両脚支持割合は、立脚時間における両脚支持期の時間の割合である。単脚支持割合は、立脚時間における単脚支持期の時間の割合である。 Returning to FIG. 11, the stance phase data analysis unit 504 then, as a time parameter, “left foot average stance time”, “right foot average stance time”, “both leg support ratio”, “left foot single leg support ratio”, which is an average of a plurality of steps. "Right foot single leg support ratio" is acquired (step S115). FIG. 16 shows the ground contact state of the left and right feet. The time from one foot to the floor is the standing time, the time when both feet are grounded is the both-leg support period, and the time when only one foot is grounded is the single-leg support. It is a period. The both-leg support ratio is a ratio of the time of the both-leg support period in the stance time. The monopod support ratio is a ratio of the time of the single leg support period in the stance time.
　図１１に戻り、次いで、立脚期データ解析部５０４は、取得された足底圧パラメータ、ＣＯＰパラメータ、時間パラメータと、解析時に用いられた歩行一定時間内の立脚期の数である歩数とを歩行処理後データ５２７に記録する（ステップＳ１１６）。 Returning to FIG. 11, the stance phase data analysis unit 504 then walks the acquired plantar pressure parameter, COP parameter, and time parameter, and the number of steps that are the number of stance phases within a predetermined walking time used in the analysis. It records in the post-processing data 527 (step S116).
　＜静止立位データ解析部５０５による処理＞
　静止立位データ解析部５０５は、立位姿勢が一定期間維持された際に取得された計測データ５２６を解析し、静止立位処理後データ５２８を生成する。 <Processing by Static Standing Data Analysis Unit 505>
The static standing data analysis unit 505 analyzes the measurement data 526 acquired when the standing posture is maintained for a certain period, and generates post-static standing processing data 528.
　図１７は静止立位データ解析部５０５の処理例を示すフローチャートである。図１７において、静止立位データ解析部５０５は、計測データ５２６の各センサの圧力値の波形データを取得し（ステップＳ１２１）、ＣＯＰ軌跡データ（「両足足圧中心ＣＯＰ_Ｘ座標値」「両足足圧中心ＣＯＰ_Ｙ座標値」）を取得する（ステップＳ１２２）。ここでのＣＯＰ軌跡は、両足で立っている状態であるため、両足の中心付近に位置する。図１８にはＣＯＰ軌跡の例が示されており、若干右足寄りで変動するものとなっている。 FIG. 17 is a flowchart showing a processing example of the stationary standing data analysis unit 505. In FIG. 17, the stationary standing data analysis unit 505 acquires waveform data of the pressure value of each sensor of the measurement data 526 (step S121), and COP trajectory data (“both foot foot pressure center COP_X coordinate value” “both foot foot pressure”. The center COP_Y coordinate value ") is acquired (step S122). The COP trajectory here is in a state of standing on both feet, and is therefore located near the center of both feet. FIG. 18 shows an example of a COP locus, which fluctuates slightly toward the right foot.
　図１７に戻り、次いで、静止立位データ解析部５０５は、ＣＯＰ軌跡から、ＣＯＰパラメータの一つとして、ＣＯＰ総軌跡長を取得する（ステップＳ１２３）。すなわち、計測データ５２６のサンプリング周期毎のＣＯＰのｘ座標とｙ座標の変位から微小移動量が求められ、それが積算されることでＣＯＰ総軌跡長が求められる。 Returning to FIG. 17, the stationary standing data analysis unit 505 then acquires the COP total trajectory length as one of the COP parameters from the COP trajectory (step S123). That is, the minute movement amount is obtained from the displacement of the COP x-coordinate and y-coordinate for each sampling period of the measurement data 526, and the total movement length is obtained by integrating the minute movement amount.
　次いで、静止立位データ解析部５０５は、ＣＯＰパラメータの一つとして、ＣＯＰ矩形面積を取得する（ステップＳ１２４）。ＣＯＰ矩形面積は、ＣＯＰ軌跡の外接矩形の面積として求められる。 Next, the stationary standing data analysis unit 505 acquires a COP rectangular area as one of the COP parameters (step S124). The COP rectangular area is obtained as an area of a circumscribed rectangle of the COP locus.
　次いで、静止立位データ解析部５０５は、足底圧パラメータの一つとして、各センサの荷重比率を取得する（ステップＳ１２５）。各センサの荷重比率は、各センサの平均圧力値が全センサ（両足の１４個のセンサ）の平均圧力値の総和で除算されることで求められる。図１８には各センサ（丸で図示）に荷重比率の値の例が示されている。 Next, the static standing data analysis unit 505 acquires the load ratio of each sensor as one of the plantar pressure parameters (step S125). The load ratio of each sensor is obtained by dividing the average pressure value of each sensor by the sum of the average pressure values of all sensors (14 sensors on both feet). FIG. 18 shows an example of load ratio values for each sensor (illustrated by a circle).
　図１７に戻り、次いで、静止立位データ解析部５０５は、取得された足底圧パラメータ、ＣＯＰパラメータを静止立位処理後データ５２８に記録する（ステップＳ１２６）。 Returning to FIG. 17, the stationary standing data analysis unit 505 then records the acquired plantar pressure parameter and COP parameter in the stationary standing post-processing data 528 (step S <b> 126).
　＜前足部局所荷重評価部５０８による処理＞
　前足部局所荷重評価部５０８は、歩行処理後データ５２７に基づいて前足部の局所的荷重の有無、ひいては胼胝や魚の目等の異常発生の可能性がないか評価を行う。 <Processing by Forefoot Local Load Evaluation Unit 508>
The forefoot local load evaluating unit 508 evaluates the presence or absence of a local load on the forefoot part based on the post-walking data 527, and whether or not there is a possibility of occurrence of abnormalities such as eyelids or fish eyes.
　図１９は前足部局所荷重評価部５０８の処理例を示すフローチャートであり、前足部の局所的荷重の有無を評価し、胼胝発生の可能性を判定する例である。ここでは、前足部の３か所のセンサからの最大圧力値平均に基づき、センサごとの値の大小の比較により局所的荷重の有り無しが評価される。 FIG. 19 is a flowchart showing a processing example of the forefoot local load evaluation unit 508, which is an example of evaluating the possibility of wrinkles by evaluating the presence or absence of a local load on the forefoot. Here, the presence or absence of a local load is evaluated by comparing the values of each sensor based on the average of the maximum pressure values from the three sensors on the forefoot.
　図１９において、前足部局所荷重評価部５０８は、歩行処理後データ５２７より前足部（３、５、７番センサ）の最大圧力値平均を参照する（ステップＳ２１１）。 In FIG. 19, the forefoot local load evaluating unit 508 refers to the maximum pressure value average of the forefoot (3rd, 5th, and 7th sensors) from the post-walking data 527 (step S211).
　次いで、前足部局所荷重評価部５０８は、３センサ中の最大値を示すセンサおよびその値をＳ、他の２つのセンサおよびその値をａ、ｂとする（ステップＳ２１２）。 Next, the forefoot local load evaluating unit 508 sets the sensor indicating the maximum value among the three sensors and its value as S, and the other two sensors and their values as a and b (step S212).
　次いで、前足部局所荷重評価部５０８は、Ｓと２ａ、Ｓと２ｂの荷重比率を判断する（ステップＳ２１３）。 Next, the forefoot local load evaluating unit 508 determines the load ratio between S and 2a, and S and 2b (step S213).
　前足部局所荷重評価部５０８は、Ｓが２ａより大きく、かつＳが２ｂより大きいという条件を満たさない場合（ステップＳ２１３のＮＯ）、正常荷重であるとする（ステップＳ２１４）。 When the forefoot local load evaluating unit 508 does not satisfy the condition that S is greater than 2a and S is greater than 2b (NO in step S213), the forefoot local load evaluating unit 508 assumes a normal load (step S214).
　また、Ｓが２ａより大きく、かつＳが２ｂより大きいという条件を満たす場合（ステップＳ２１３のＹＥＳ）、前足部局所荷重評価部５０８は、センサＳへの局所的荷重があるとし（ステップＳ２１５）、センサＳのエリアでの胼胝発生の可能性があると評価する（ステップＳ２１６）。 Further, when the condition that S is larger than 2a and S is larger than 2b (YES in step S213), the forefoot local load evaluating unit 508 has a local load on the sensor S (step S215). It is evaluated that there is a possibility of occurrence of wrinkles in the area of the sensor S (step S216).
　＜回内足・回外足評価部５０９による処理＞
　回内足・回外足評価部５０９は、歩行処理後データ５２７に基づいて回内足・回外足の異常がないか評価を行う。 <Processing by the pronation / extroversion foot evaluation unit 509>
The pronation / extroversion foot evaluation unit 509 evaluates whether there is an abnormality in the pronation / extroversion foot based on the post-walking data 527.
　図２０は回内足・回外足評価部５０９の処理例を示すフローチャートである。図２０において、回内足・回外足評価部５０９は、歩行処理後データ５２７よりＣＯＰ屈曲角を参照する（ステップＳ２２１）。 FIG. 20 is a flowchart showing a processing example of the pronation / extroversion foot evaluation unit 509. In FIG. 20, the pronation / extroversion foot evaluation unit 509 refers to the COP bending angle from the post-walking processing data 527 (step S221).
　次いで、回内足・回外足評価部５０９は、ＣＯＰ屈曲角が１５５より小さいか否か判断し（ステップＳ２２２）、ＣＯＰ屈曲角が１５５より小さいと判断した場合（ステップＳ２２２のＹＥＳ）、回外足の可能性があると評価する（ステップＳ２２３）。 Next, the pronation / extroversion foot evaluation unit 509 determines whether or not the COP flexion angle is smaller than 155 (step S222), and if it is determined that the COP flexion angle is smaller than 155 (YES in step S222), It is evaluated that there is a possibility of an outer leg (step S223).
　また、回内足・回外足評価部５０９は、ＣＯＰ屈曲角が１５５より小さくないと判断した場合（ステップＳ２２２のＮＯ）、ＣＯＰ屈曲角が１７５より大きいか否か判断し（ステップＳ２２４）、ＣＯＰ屈曲角が１７５より大きいと判断した場合（ステップＳ２２４のＹＥＳ）、回内足の可能性があると評価する（ステップＳ２２５）。回内足・回外足評価部５０９は、ＣＯＰ屈曲角が１７５より大きくないと判断した場合（ステップＳ２２４のＮＯ）、正常と評価する（ステップＳ２２６）。 In addition, when the pronation foot / exterior foot evaluation unit 509 determines that the COP bending angle is not smaller than 155 (NO in step S222), it determines whether or not the COP bending angle is larger than 175 (step S224). If it is determined that the COP bending angle is greater than 175 (YES in step S224), it is evaluated that there is a possibility of a pronation foot (step S225). When determining that the COP flexion angle is not larger than 175 (NO in step S224), the pronation / extroversion foot evaluation unit 509 evaluates normal (step S226).
　＜実験結果＞
　回内足・回外足評価部５０９によって、７２４人について回内足・回外足の異常を評価した結果、８８％乃至９０％の精度で評価することができた。 <Experimental result>
As a result of evaluating abnormalities of the pronation and supination legs for 724 people by the pronation and supination legs evaluation unit 509, it was possible to evaluate with an accuracy of 88% to 90%.
　＜複数歩バランス評価部５１０による処理＞
　複数歩バランス評価部５１０は、歩行処理後データ５２７に基づいて複数歩バランスを評価する。 <Processing by Multi-Step Balance Evaluation Unit 510>
The multi-step balance evaluation unit 510 evaluates the multi-step balance based on the post-walking processing data 527.
　図２１は複数歩バランス評価部５１０の処理例を示すフローチャートである。図２１において、複数歩バランス評価部５１０は、歩行処理後データ５２７より歩行中期ＣＯＰ_Ｘ座標範囲を参照し（ステップＳ２３１）、歩行中期ＣＯＰ_Ｘ座標範囲が１より大きいか否か判断する（ステップＳ２３２）。 FIG. 21 is a flowchart showing a processing example of the multi-step balance evaluation unit 510. In FIG. 21, the multi-step balance evaluation unit 510 refers to the intermediate walking period COP_X coordinate range from the post-walking data 527 (step S231), and determines whether the intermediate walking period COP_X coordinate range is greater than 1 (step S232).
　そして、複数歩バランス評価部５１０は、歩行中期ＣＯＰ_Ｘ座標範囲が１より大きいと判断した場合（ステップＳ２３２のＹＥＳ）、歩行立脚時の複数歩バランスのばらつきが大きいと評価する（ステップＳ２３３）。また、複数歩バランス評価部５１０は、歩行中期ＣＯＰ_Ｘ座標範囲が１より大きくないと判断した場合（ステップＳ２３２のＮＯ）、歩行立脚時の複数歩バランスのばらつきが小さいと評価する（ステップＳ２３４）。 Then, when the multi-step balance evaluation unit 510 determines that the mid-walk COP_X coordinate range is larger than 1 (YES in step S232), the multi-step balance evaluation unit 510 evaluates that the variation in the multi-step balance when walking is large (step S233). Further, when determining that the middle walking COP_X coordinate range is not larger than 1 (NO in step S232), the multiple-step balance evaluation unit 510 evaluates that the variation in the multiple-step balance when walking is small (step S234).
　図２２及び図２３はＣＯＰのばらつきの例を示す図であり、図２２は複数歩のＣＯＰのばらつきが少ない例、図２３は複数歩のＣＯＰのばらつきが多い例を示している。 22 and FIG. 23 are diagrams showing examples of COP variation, FIG. 22 shows an example where COP variation at a plurality of steps is small, and FIG. 23 shows an example where COP variation at a plurality of steps is large.
　＜立脚時間評価部５１１による処理＞
　立脚時間評価部５１１は、歩行処理後データ５２７に基づいて立脚時間を評価する。 <Processing by Standing Time Evaluation Unit 511>
The stance time evaluation unit 511 evaluates the stance time based on the post-walking processing data 527.
　図２４は立脚時間評価部５１１の処理例を示すフローチャートである。図２４において、立脚時間評価部５１１は、歩行処理後データ５２７より時間パラメータとしての立脚時間を参照し（ステップＳ２４１）、立脚時間の正常値（例えば、６００～９００ｍｓ）と比較して正常またはそれより短いもしくは長いなどの評価を行う（ステップＳ２４２）。 FIG. 24 is a flowchart showing a processing example of the stance time evaluation unit 511. In FIG. 24, the stance time evaluation unit 511 refers to the stance time as a time parameter from the post-walking data 527 (step S241) and compares it with the normal value of the stance time (eg, 600 to 900 ms). Evaluation such as shorter or longer is performed (step S242).
　＜両脚支持割合評価部５１２による処理＞
　両脚支持割合評価部５１２は、歩行処理後データ５２７に基づいて両脚支持割合を評価する。 <Processing by the both-leg support ratio evaluation unit 512>
The both-leg support ratio evaluation unit 512 evaluates the both-leg support ratio based on the post-walking processing data 527.
　図２５は両脚支持割合評価部５１２の処理例を示すフローチャートである。図２５において、両脚支持割合評価部５１２は、歩行処理後データ５２７より時間パラメータとしての両脚支持割合と単脚支持割合を参照し（ステップＳ２５１）、それぞれの正常値（例えば、両脚支持割合については２０～４０％、単脚支持割合については６０～８０％）と比較して正常またはそれより少ないもしくは多いなどの評価を行う（ステップＳ２５２）。 FIG. 25 is a flowchart showing a processing example of the both-leg support ratio evaluation unit 512. In FIG. 25, the both-leg support ratio evaluation unit 512 refers to the both-leg support ratio and the single-leg support ratio as time parameters from the post-walking data 527 (step S251), and each normal value (for example, about the both-leg support ratio). 20% to 40% and the single leg support ratio is 60% to 80%), and the evaluation is normal or less or more (step S252).
　＜左右差評価部５１３による処理＞
　左右差評価部５１３は、静止立位処理後データ５２８に基づいて静止立位の状態における左右差を評価する。 <Processing by Left / Right Difference Evaluation Unit 513>
The left / right difference evaluation unit 513 evaluates the left / right difference in the static standing state based on the post-static standing process data 528.
　図２６は左右差評価部５１３の処理例を示すフローチャートである。図２６において、左右差評価部５１３は、静止立位処理後データ５２８より左足１番～７番の荷重比率を参照し（ステップＳ３１１）、左足荷重比率合計(Sum_Left)を算出する（ステップＳ３１２）。 FIG. 26 is a flowchart showing a processing example of the left / right difference evaluation unit 513. In FIG. 26, the left / right difference evaluation unit 513 refers to the load ratios of the left foot 1st to 7th from the post-static standing process data 528 (step S311) and calculates the total left foot load ratio (Sum_Left) (step S312). .
　また、左右差評価部５１３は、静止立位処理後データ５２８より右足１番～７番の荷重比率を参照し（ステップＳ３１３）、右足荷重比率合計(Sum_Right)を算出する（ステップＳ３１４）。 Also, the left / right difference evaluation unit 513 refers to the load ratios of the right foot No. 1 to No. 7 from the post-static standing process data 528 (Step S313), and calculates the right foot load ratio sum (Sum_Right) (Step S314).
　次いで、左右差評価部５１３は、左足荷重比率合計(Sum_Left)から右足荷重比率合計(Sum_Right)を引いた値を判断し（ステップＳ３１５）、２０以上である場合は左足荷重であると評価し（ステップＳ３１６）、－２０以下の場合は右足荷重であると評価し（ステップＳ３１７）、－２０より大きく、かつ２０より小さい場合は正常であると評価する（ステップＳ３１８）。 Next, the left / right difference evaluation unit 513 determines a value obtained by subtracting the right foot load ratio sum (Sum_Right) from the left foot load ratio sum (Sum_Left) (step S315). If it is less than −20, it is evaluated that it is a right foot load (step S317), and if it is greater than −20 and less than 20, it is evaluated as normal (step S318).
　＜足部アーチ評価部５１４による処理＞
　足部アーチ評価部５１４は、静止立位処理後データ５２８に基づいて静止立位の状態における足部アーチの形成状態を評価し、ひいては扁平足発生の可能性がないかを評価する。 <Processing by foot arch evaluation unit 514>
The foot arch evaluation unit 514 evaluates the formation state of the foot arch in the static standing state based on the post-static standing process data 528, and evaluates whether or not a flat foot is likely to be generated.
　図２７は足部アーチ評価部５１４の処理例を示すフローチャートである。図２７において、足部アーチ評価部５１４は、静止立位処理後データ５２８より左足２番センサ：中足部１荷重比率と、左足６番センサ：中足部２荷重比率を参照する（ステップＳ３２１）。 FIG. 27 is a flowchart showing a processing example of the foot arch evaluation unit 514. In FIG. 27, the foot arch evaluation unit 514 refers to the left foot 2 sensor: middle foot 1 load ratio and the left foot 6 sensor: middle foot 2 load ratio from the post-static standing processing data 528 (step S321). ).
　次いで、足部アーチ評価部５１４は、左足２番センサと６番センサの荷重比率を比較する（ステップＳ３２２）。 Next, the foot arch evaluation unit 514 compares the load ratios of the left foot second sensor and the sixth sensor (step S322).
　足部アーチ評価部５１４は、２番センサの荷重比率が６番センサの荷重比率よりも大きい場合、左足正常と評価し（ステップＳ３２３）、２番センサの荷重比率が６番センサの荷重比率以下である場合、左足の足部アーチ形成不十分と評価する（ステップＳ３２４）。 When the load ratio of the second sensor is larger than the load ratio of the sixth sensor, the foot arch evaluation unit 514 evaluates that the left foot is normal (step S323), and the load ratio of the second sensor is equal to or less than the load ratio of the sixth sensor. If it is, it is evaluated that the foot arch formation of the left foot is insufficient (step S324).
　その後、足部アーチ評価部５１４は、静止立位処理後データ５２８より右足２番センサ：中足部１荷重比率と、右足６番センサ：中足部２荷重比率を参照する（ステップＳ３２５、Ｓ３２６）。 Thereafter, the foot arch evaluation unit 514 refers to the right foot 2 sensor: middle foot 1 load ratio and the right foot 6 sensor: middle foot 2 load ratio from the post-static standing process data 528 (steps S325 and S326). ).
　次いで、足部アーチ評価部５１４は、右足２番センサと６番センサ荷重比率を比較する（ステップＳ３２７、Ｓ３２８）。 Next, the foot arch evaluation unit 514 compares the right foot second sensor and the sixth sensor load ratio (steps S327 and S328).
　足部アーチ評価部５１４は、左足正常と評価（ステップＳ３２３）した後の比較（ステップＳ３２７）において、２番センサの荷重比率が６番センサの荷重比率以下である場合、左足正常で右足アーチ形成不十分と評価し（ステップＳ３２９）、この場合、右足が扁平足である可能性ありと判定する（ステップＳ３３０）。また、２番センサの荷重比率が６番センサの荷重比率よりも大きい場合、両足正常と評価する（ステップＳ３３１）。 The foot arch evaluation unit 514 determines that the left foot is normal and the right foot arch is formed when the load ratio of the 2nd sensor is equal to or less than the load ratio of the 6th sensor in the comparison (step S327) after the evaluation that the left foot is normal (step S323). Evaluation is insufficient (step S329), and in this case, it is determined that the right foot may be a flat foot (step S330). If the load ratio of the second sensor is larger than the load ratio of the sixth sensor, it is evaluated that both feet are normal (step S331).
　また、足部アーチ評価部５１４は、左足アーチ形成不十分と評価（ステップＳ３２４）した後の比較（ステップＳ３２８）において、２番センサの荷重比率が６番センサの荷重比率よりも大きい場合、左足アーチ形成不十分で右足正常と評価し（ステップＳ３３２）、この場合、左足が扁平足である可能性ありと判定する（ステップＳ３３３）。また、２番センサの荷重比率が６番センサの荷重比率以下である場合、両足のアーチが形成不十分であると評価し（ステップＳ３３４）、両足扁平足の可能性があると判定する（ステップＳ３３５）。 Also, the foot arch evaluation unit 514 determines that the left foot arch formation is insufficient (step S324) and the comparison is made after the load ratio of the second sensor is larger than the load ratio of the sixth sensor in the comparison (step S328). It is evaluated that the right foot is normal with insufficient arch formation (step S332), and in this case, it is determined that the left foot may be a flat foot (step S333). Further, when the load ratio of the second sensor is equal to or less than the load ratio of the sixth sensor, it is evaluated that the arch of both feet is insufficiently formed (step S334), and it is determined that there is a possibility of both feet flat feet (step S335). ).
　＜実験結果＞
　足部アーチ評価部５１４によって、７２４人について扁平足を評価した結果、９１．５％の精度で評価することができた。 <Experimental result>
As a result of evaluating flat feet for 724 people by the foot arch evaluation unit 514, it was possible to evaluate with an accuracy of 91.5%.
　＜母趾接地評価部５１７による処理＞
　図２８は母趾接地評価部５１７の処理例を示すフローチャートである。図２８において、母趾接地評価部５１７は、歩行処理後データ５２７より左足４番センサ：前足部２最大圧力値平均と、右足４番センサ：前足部２最大圧力値平均を参照する（ステップＳ３４１）。 <Processing by the toe contact evaluation unit 517>
FIG. 28 is a flowchart showing a processing example of the toe contact evaluation unit 517. In FIG. 28, the toe contact evaluation unit 517 refers to the left foot 4 sensor: forefoot 2 maximum pressure value average and the right foot 4 sensor: forefoot 2 maximum pressure value average from the post-walking data 527 (step S341). ).
　次いで、母趾接地評価部５１７は、両センサが示す圧力値を合計した合計値に基づいて、母趾の接地を評価する（ステップＳ３４２）。例えば、評価は、合計値と、あらかじめ設定される所定値とを比較して行われる。以下、説明では、所定値は、５Ｎ（ニュートン）であるとする。ただし、所定値は、設定可能な値であり、例えば、体重等によって定まる値となる。なお、所定値を「５Ｎ」とする場合は、体重が「４０ｋｇ乃至５０ｋｇ」程度の場合である。また、所定値は、「５０ｋＰａ」としてもよい。すなわち、評価は、力又は圧力のどちらで行われてもよい。 Next, the mother's ground contact evaluation unit 517 evaluates the ground contact of the mother's body based on the total value of the pressure values indicated by both sensors (step S342). For example, the evaluation is performed by comparing the total value with a predetermined value set in advance. In the following description, it is assumed that the predetermined value is 5N (Newton). However, the predetermined value is a value that can be set, for example, a value determined by weight or the like. When the predetermined value is “5N”, the weight is about “40 kg to 50 kg”. The predetermined value may be “50 kPa”. That is, the evaluation may be performed with either force or pressure.
　例えば、両センサが示す圧力値の合計値が５Ｎ以上であると評価される場合、母趾接地評価部５１７は、母趾の接地が通常と評価する（ステップＳ３４３）。 For example, when it is evaluated that the total value of the pressure values indicated by both sensors is 5N or more, the toe ground contact evaluation unit 517 evaluates that the toe contact is normal (step S343).
　また、両センサが示す圧力値の合計値が５Ｎ未満かつ０Ｎより大きいと評価される場合、母趾接地評価部５１７は、母趾の接地が弱く、浮指気味であると評価する（ステップＳ３４４）。 Further, when it is evaluated that the total value of the pressure values indicated by both sensors is less than 5N and greater than 0N, the toe ground contact evaluation unit 517 evaluates that the toe touch is weak and that the toe is slightly floating (step S344). ).
　さらに、両センサが示す圧力値の合計値が０Ｎ、すなわち、出力がないと評価される場合、母趾接地評価部５１７は、母趾の接地がなく、浮指であると評価する（ステップＳ３４５）。 Furthermore, when the total value of the pressure values indicated by both sensors is 0 N, that is, when it is evaluated that there is no output, the toe ground contact evaluation unit 517 evaluates that the toe is not touched and is a floating finger (step S345). ).
　以上のようにして、母趾接地評価部５１７は、母趾接地の評価結果を出力する。 As described above, the mother ground contact evaluation unit 517 outputs the evaluation result of the mother ground contact.
　＜総合評価部５１５による処理＞
　総合評価部５１５は、前足部局所荷重評価部５０８、回内足・回外足評価部５０９、複数歩バランス評価部５１０、立脚時間評価部５１１、両脚支持割合評価部５１２、左右差評価部５１３、足部アーチ評価部５１４による評価結果と、母趾接地評価部５１７による評価結果と、ライフログデータ５２４とを参照し、ユーザ毎の総合評価を行う。 <Processing by comprehensive evaluation unit 515>
The comprehensive evaluation unit 515 includes a forefoot local load evaluation unit 508, a pronation foot / extraction foot evaluation unit 509, a multi-step balance evaluation unit 510, a stance time evaluation unit 511, a both-leg support ratio evaluation unit 512, and a left-right difference evaluation unit 513. The overall evaluation for each user is performed with reference to the evaluation result by the foot arch evaluation unit 514, the evaluation result by the toe contact evaluation unit 517, and the life log data 524.
　図２９は総合評価部５１５の処理例を示すフローチャートである。図２９において、総合評価部５１５は、複数歩バランス評価部５１０の評価結果と、左右差評価部５１３の評価結果とに基づき、カラダバランス度を算出する（ステップＳ４１）。具体的には、それぞれの評価結果（数値化されていないものは数値化を実施）の加算・乗算・規格化等により、最終的に例えば１～１０の値に点数化して、カラダバランス度を算出する。 FIG. 29 is a flowchart showing a processing example of the comprehensive evaluation unit 515. In FIG. 29, the comprehensive evaluation unit 515 calculates the body balance degree based on the evaluation result of the multi-step balance evaluation unit 510 and the evaluation result of the left / right difference evaluation unit 513 (step S41). Specifically, by adding, multiplying, standardizing, etc. each evaluation result (the one that has not been digitized is digitized), it is finally scored to a value of 1 to 10, for example, to improve the body balance degree. calculate.
　次いで、総合評価部５１５は、立脚時間評価部５１１の評価結果と、両脚支持割合評価部５１２の評価結果とに基づき、歩き方リズム（度）を算出する（ステップＳ４２）。具体的には、それぞれの評価結果（数値化されていないものは数値化を実施）の加算・乗算・規格化等により、最終的に例えば１～１０の値に点数化して、歩き方リズムを算出する。 Next, the comprehensive evaluation unit 515 calculates a walking rhythm (degree) based on the evaluation result of the stance time evaluation unit 511 and the evaluation result of the both leg support ratio evaluation unit 512 (step S42). Specifically, by adding, multiplying, standardizing, etc. each evaluation result (unquantized ones are digitized), the score is finally converted to a value of 1 to 10, for example, to change the walking rhythm calculate.
　次いで、総合評価部５１５は、前足部局所荷重評価部５０８の評価結果と、回内足・回外足評価部５０９の評価結果と、足部アーチ評価部５１４の評価結果とに基づき、美足度を算出する（ステップＳ４３）。具体的には、それぞれの評価結果（数値化されていないものは数値化を実施）の加算・乗算・規格化等により、最終的に例えば１～１０の値に点数化して、美足度を算出する。なお、一般的な健常足と比較した場合の類似度や解剖学的に異常な荷重が行われていないか等が美足度の算出に考慮されるようにしてもよい。また、美足度は、足が健康な状態であると高くなる値である。 Next, the comprehensive evaluation unit 515 determines the beauty foot based on the evaluation result of the forefoot local load evaluation unit 508, the evaluation result of the pronation foot / extroversion foot evaluation unit 509, and the evaluation result of the foot arch evaluation unit 514. The degree is calculated (step S43). Specifically, by adding, multiplying, standardizing, etc. each evaluation result (the one that has not been digitized is digitized), it is finally scored to a value of 1 to 10, for example, to improve the leg calculate. It should be noted that the degree of similarity may be taken into account when calculating the degree of beauty, such as the degree of similarity compared to a normal healthy foot or whether an anatomically abnormal load is applied. The degree of beauty is a value that increases when the foot is in a healthy state.
　なお、美足度は、胼胝・魚の目評価結果と、回内足・回外足評価結果と、足部アーチ評価結果と、母趾接地の評価結果のうち、少なくとも１つを用いて算出される。そして、算出は、評価結果を重み付けしてもよい。すなわち、美足度の算出には、母趾接地の評価結果が加わってもよい。 The degree of beauty foot is calculated by using at least one of the eye / fish eye evaluation result, the pronation / extraction foot evaluation result, the foot arch evaluation result, and the evaluation result of the toe contact with the heel. . In the calculation, the evaluation result may be weighted. That is, the evaluation result of the toe contact may be added to the calculation of the degree of beauty.
　また、足部アーチ評価結果は、他の評価結果と比較して、健康状態を示す値として重みが大きい。したがって、美足度は、足部アーチ評価結果のみで算出されたり、足部アーチ評価結果の重みを高くして算出されたりしてもよい。このような算出方法であると、精度良く美足度を算出できる。 Also, the foot arch evaluation result has a higher weight as a value indicating the health condition than the other evaluation results. Therefore, the leg degree may be calculated only from the foot arch evaluation result, or may be calculated by increasing the weight of the foot arch evaluation result. With such a calculation method, it is possible to accurately calculate the degree of beauty.
　次いで、総合評価部５１５は、ライフログデータ５２４を参照し、活動度を算出する（ステップＳ４４）。具体的には、直近の所定期間内の移動距離、歩数、平均歩行速度等の加算・乗算・規格化等により、最終的に例えば１～１０の値に点数化して、活動度を算出する。 Next, the comprehensive evaluation unit 515 refers to the life log data 524 and calculates an activity level (step S44). Specifically, the degree of activity is calculated by finally scoring to a value of 1 to 10, for example, by adding, multiplying, normalizing, etc. the moving distance, the number of steps, and the average walking speed within the most recent predetermined period.
　算出された総合評価の結果（美足度、カラダバランス度、歩き方リズム、活動度）は、図３０に示されるようなグラフ（レーダーチャート）へのプロットにより表示（可視化）が可能である。 The calculated comprehensive evaluation results (foot degree, body balance degree, walking rhythm, activity level) can be displayed (visualized) by plotting on a graph (radar chart) as shown in FIG.
　＜総合分析部５１６による処理＞
　総合分析部５１６は、上述した処理により得られたデータとその他の追加的なデータに基づき、歩行動作・日常生活活動データの総合分析を行う。追加的なデータとしては、ユーザデータ５２２や靴データ５２３やライフログデータ５２４がある。「１日の予定」「目的地」等はユーザの手入力およびユーザが利用する任意のスケジュール機能やカレンダー機能を持つインターネットサービスに記録された情報を取得するなどして利用する。「移動距離」「歩数」「平均歩行速度」「最多位置情報（ＧＰＳ）」等は情報端末３またはユーザが利用する任意のウェアラブル情報端末から取得され、また取得情報から計算されて記録される。「靴メーカー型番」等はユーザの手入力や靴製品バーコードの読み取り等により情報取得が行われる。また、靴購入後１週間～１カ月などに「靴擦れ有無」「靴擦れ部位」「胼胝有無」「胼胝部位」「痛み有無」「痛み部位」「圧迫有無」「圧迫部位」等の情報がユーザにより入力される。 <Processing by Comprehensive Analysis Unit 516>
The comprehensive analysis unit 516 performs a comprehensive analysis of walking motion / daily life activity data based on the data obtained by the above-described processing and other additional data. Additional data includes user data 522, shoe data 523, and life log data 524. “Daily schedule”, “Destination”, etc. are used by manually inputting the user and acquiring information recorded in an Internet service having an arbitrary schedule function or calendar function used by the user. “Movement distance”, “number of steps”, “average walking speed”, “most frequent position information (GPS)” and the like are acquired from the information terminal 3 or any wearable information terminal used by the user, and are calculated from the acquired information and recorded. “Shoemaker model number” and the like are obtained by manual input by a user, reading of a shoe product barcode, or the like. In addition, information such as “Shoe rubbing presence / absence”, “Shoe rubbing site”, “Wheel presence / absence”, “Hail location”, “Pain presence / absence”, “Pain location”, “Pressure presence / absence”, “Pressure location”, etc. Entered.
　分析の例として、総合分析部５１６は、本システムのユーザの現在の足部状態に類似している足部状態を持ち、かつ行動量が近い他者が、類似時点より後の時点でどのような足部状態に遷移したかを特定し、未来の足部状態の予測判定をすることができる。 As an example of analysis, the comprehensive analysis unit 516 has a foot state that is similar to the current foot state of the user of the system, and how the other person who has a close amount of action is later than the similar time point. It is possible to determine whether or not the foot state has been changed, and to predict and predict the future foot state.
　［分析の具体例＃１：他者との比較］
　総合評価部５１５による総合評価までの処理を、蓄積しているすべてのユーザの歩行処理後データ５２７に対して行い、総合分析部５１６はクラスタリングを行うことで類似傾向のグループを分けることができる。総合分析部５１６は、その結果を、本システムのユーザに対し、属しているグループの全体に占める割合（多さ少なさ）や良好なグループの総合評価指標分布に対する距離として示すことができる。このフィードバックには、図３０に示されたようなレーダーチャートを利用することができる。 [Specific Example of Analysis # 1: Comparison with Others]
The process up to the comprehensive evaluation by the comprehensive evaluation unit 515 is performed on the accumulated post-walking data 527 of all the users, and the comprehensive analysis unit 516 can divide groups of similar tendencies by performing clustering. The comprehensive analysis unit 516 can show the result as a ratio to the total number of groups to which the user belongs (small or large) and a distance to a comprehensive evaluation index distribution of a good group. A radar chart as shown in FIG. 30 can be used for this feedback.
　［分析の具体例＃２：靴の推奨］
　総合分析部５１６は、他の分析結果のフィードバックとともに、あるいは個別の要求に応じて、ユーザに相応しい靴の候補を提示する。 [Specific example # 2: Recommended shoes]
The comprehensive analysis unit 516 presents shoes candidates suitable for the user together with feedback of other analysis results or in response to individual requests.
　成人女性において、約７割が靴選びで後悔するという結果が得られており、その要因の一つとして個人の主観や経験で靴や履物の選択が行われている点が挙げられる。また、靴販売店で一時的な足部形状計測は行われているものの、日常活動中の歩行データに基づいた靴の選択は行われていない。ここでは、客観的かつ定量的な歩行運動データに基づいて評価された個人の歩行・足部状態の特徴から、他者との比較により、類似する状態の他者であって、その後に障害等が発生していない健常足の持ち主が類似していた時期以降に履いていた靴を提案することで、適切な靴選択を支援するようにしている。 As a result, about 70% of adult women regret selecting shoes. One of the factors is the selection of shoes and footwear based on individual subjectivity and experience. Moreover, although the foot shape measurement is performed temporarily at a shoe store, the selection of shoes based on walking data during daily activities is not performed. Here, based on objective and quantitative walking movement data, the characteristics of the individual's walking / foot state are evaluated by comparison with others, and the other person is in a similar state. By proposing shoes that were worn since the time when the owners of healthy feet that did not have any similarities were proposed, appropriate shoe selection was supported.
　図３１は靴の推奨の処理例を示すフローチャートである。図３１において、総合分析部５１６は、対象ユーザに対し、歩行・静止立位の状態が類似する他ユーザを抽出する（ステップＳ５１）。この場合の類似は、ユーザデータ５２２から「足長」「足幅」「足高」「足囲」の情報を取り出し、足の外形の類似をみてもよいし、歩行処理後データ５２７および静止立位処理後データ５２８から各パラメータの類似をみてもよい。 FIG. 31 is a flowchart showing an example of recommended shoe processing. In FIG. 31, the comprehensive analysis unit 516 extracts other users who are similar in a walking / still standing state to the target user (step S51). Similarity in this case may be obtained by extracting information on “foot length”, “foot width”, “foot height”, and “foot circumference” from the user data 522, and looking at the similarity of the outer shape of the foot. Similarity of each parameter may be seen from post-position processing data 528.
　次いで、総合分析部５１６は、評価部５０７または総合評価部５１５の評価結果が類似する他ユーザに絞り込みを行う（ステップＳ５２）。 Next, the comprehensive analysis unit 516 narrows down to other users who have similar evaluation results from the evaluation unit 507 or the comprehensive evaluation unit 515 (step S52).
　次いで、総合分析部５１６は、絞り込まれた他ユーザのうち、類似するとされた時期以降に障害の評価が行われていない者に絞り込みを行う（ステップＳ５３）。 Next, the comprehensive analysis unit 516 narrows down the narrowed-down users to those who have not been evaluated for failures after the time when they are regarded as similar (step S53).
　次いで、総合分析部５１６は、絞り込まれた他ユーザの、類似するとされた時期以降に使用されていた靴データ５２３を取得し、推奨される靴選択候補として提示する（ステップＳ５４）。 Next, the comprehensive analysis unit 516 acquires the shoe data 523 that has been used since the similar time of other narrowed-down users and presents it as a recommended shoe selection candidate (step S54).
　なお、総合分析部５１６は、ユーザデータ５２２から対象ユーザと他ユーザの性別や年齢・世代等の情報を取得し、更にはライフログデータ５２４から行動パターンを示す各種の情報を取得し、それらが類似している他ユーザに更に絞り込みを行うようにしてもよい。 The comprehensive analysis unit 516 acquires information such as the sex and age / generation of the target user and other users from the user data 522, and further acquires various types of information indicating behavior patterns from the life log data 524. You may make it narrow down further to other similar users.
　なお、靴の候補が提示される場合についての処理例であったが、インソール、足指矯正パッド、外反母趾用サポーター等の補助装具についても、同様に提示されるようにしてもよい。 In addition, although it was the example of a process about the case where the candidate of shoes is shown, you may make it show similarly about auxiliary devices, such as an insole, a toe correction pad, and a supporter for hallux valgus.
　［分析の具体例＃３：靴の使用による足部異常の予測］
　総合分析部５１６は、例えば、次のような手順で靴の使用による足部異常を予測する。 [Specific Example of Analysis # 3: Prediction of foot abnormalities due to use of shoes]
For example, the comprehensive analysis unit 516 predicts foot abnormalities due to the use of shoes in the following procedure.
　１．ユーザデータ５２２から「足長」「足幅」「足高」「足囲」の情報を取り出し、他者間比較で足の外形が類似する他者を特定する。 1. Information on “foot length”, “foot width”, “foot height”, and “foot circumference” is extracted from the user data 522, and another person whose foot shape is similar is identified by comparison between others.
　２．上記１．で特定した他者の処理後データ（過去のデータから本システムのユーザの利用時までのすべての時系列データを含む）から、靴データ５２３を用い、本システムのユーザが選択している靴と類似した靴を使用しているときに取得された歩行処理後データ５２７および静止立位処理後データ５２８を特定する。 2. Above 1. The shoes selected by the user of this system using the shoe data 523 from the post-processing data of other persons specified in (including all time series data from the past data to the time of use of the user of this system) The post-walking processing data 527 and the stationary standing processing post-processing data 528 acquired when using similar shoes are specified.
　３．上記２．で特定した処理後データから、本システムのユーザと歩行機能が類似するデータを選び出す。処理内容については後述する。 3. 2. From the post-processing data specified in step 1, data having a walking function similar to that of the user of this system is selected. The processing contents will be described later.
　４．類似該当データがある場合、その該当ユーザ（ユーザ群）が、類似した靴を使用していた際の足部と靴に関する情報（具体的には「胼胝形成」、「靴擦れの有無」、「靴の圧迫」、「痛み」の情報）を捉えることで、本システムのユーザが選択している靴を使用継続した場合に足部異常発生の可能性ありあるいはなしと判定する。この場合、「胼胝形成の有無」、「靴擦れの有無」、「圧迫の有無」、「痛みの有無」の可能性が予測でき、履き始めてから発生に至るまでの、経時的な予測が可能となる。 4. If there is similar applicable data, the relevant user (user group) has information about the feet and shoes when using similar shoes (specifically, “formation of wrinkles”, “presence / absence of shoe rubs”, “shoes” ”Pressure” and “pain” information), it is determined that there is a possibility that the foot abnormalities may occur or not when the shoes selected by the user of the system are used. In this case, the possibility of “presence / absence of wrinkle formation”, “presence / absence of shoe rub”, “presence / absence of pressure”, and “presence / absence of pain” can be predicted, and it is possible to predict over time from the start of wearing to the occurrence of occurrence. Become.
　図３２は上述の処理「３．」での歩行機能の類似する対象データ決定の処理例を示すフローチャートである。図３２において、総合分析部５１６は、本システムのユーザＡと他者のユーザ群Ｂについて、歩行処理後データ５２７により両足３、４、５、７番センサの最大圧力値平均とＣＯＰ屈曲角とを抽出・参照する（ステップＳ６１）。 FIG. 32 is a flowchart showing a processing example of target data determination similar to the walking function in the above-described processing “3.”. In FIG. 32, the comprehensive analysis unit 516 uses the post-walking processing data 527 for the user A and the other user group B of this system to calculate the maximum pressure value average of the sensors of the feet 3, 4, 5, and 7 and the COP flexion angle. Are extracted and referenced (step S61).
　次いで、総合分析部５１６は、両足３、４、５、７番センサの最大圧力値平均から離床時エリアを特定する（ステップＳ６２）。 Next, the comprehensive analysis unit 516 specifies the bed leaving area from the average maximum pressure value of the sensors of the three feet 3, 4, 5, and 7 (step S62).
　次いで、総合分析部５１６は、Ｂ群からＡと離床時エリアが一致する対象を抽出する（ステップＳ６３）。離床時エリアが一致しない場合、処理を終了する。 Next, the comprehensive analysis unit 516 extracts a target in which the area at the time of bed leaving coincides with A from the group B (step S63). If the areas at the time of getting out do not match, the process ends.
　次いで、総合分析部５１６は、ＣＯＰ屈曲角が１７５より大きい場合は歩行タイプＡ、ＣＯＰ屈曲角が１５５より小さい場合は歩行タイプＢ、それ以外の場合は歩行タイプＣとする（ステップＳ６４）。 Next, the comprehensive analysis unit 516 sets walking type A when the COP bending angle is larger than 175, walking type B when the COP bending angle is smaller than 155, and walking type C otherwise (step S64).
　次いで、総合分析部５１６は、Ｂ群からＡと歩行タイプ（Ａ～Ｃ）が一致する対象を抽出し（ステップＳ６５）、Ａに対する比較対象を決定する（ステップＳ６６）。歩行タイプが一致しない場合、処理を終了する。この場合、同じ歩行タイプに属するデータを持つユーザ同士は歩行機能が近いとみなすことができる。 Next, the comprehensive analysis unit 516 extracts a target in which the walking type (A to C) matches A from the group B (step S65), and determines a comparison target for A (step S66). If the walking type does not match, the process ends. In this case, users having data belonging to the same walking type can be regarded as having a walking function close to each other.
　＜総括＞
　以上説明したように、本実施形態によれば、ユーザの日常生活中における足部・歩行データの経時的変化を捉えられ、複数のユーザのデータが統一的に収集され、適切な分析・評価が行われる。より具体的には、
・足部状態や歩行機能の個人内比較だけでなく、他者データも交えた個人間比較による評価・予測が可能である。
・疾患や障害の兆候の予測による歩行の最適化（足部の痛み等の最適化）が実現可能である。
・日常生活中でユーザ自身の健康管理や靴選択に繋がる。
・子どもから高齢者まで、全ての年代層が対象となることから、子どもの発達・成長記録として経年データの分析が可能である。 <Summary>
As described above, according to the present embodiment, it is possible to capture temporal changes in foot / walking data during daily life of a user, collect data of a plurality of users uniformly, and perform appropriate analysis / evaluation. Done. More specifically,
・ Evaluation / prediction is possible not only by individual comparison of foot condition and walking function, but also by comparison between individuals with other data.
-Optimization of walking (optimization of foot pain, etc.) by predicting signs of disease or disability can be realized.
-It leads to user's own health management and shoe selection in daily life.
・ All age groups from children to the elderly are targeted, so it is possible to analyze aged data as a record of child development and growth.
　以上、本発明の好適な実施の形態により本発明を説明した。ここでは特定の具体例を示して本発明を説明したが、特許請求の範囲に定義された本発明の広範な趣旨および範囲から逸脱することなく、これら具体例に様々な修正および変更を加えることができることは明らかである。すなわち、具体例の詳細および添付の図面により本発明が限定されるものと解釈してはならない。 The present invention has been described above by the preferred embodiments of the present invention. While the invention has been described with reference to specific embodiments, various modifications and changes may be made to the embodiments without departing from the broad spirit and scope of the invention as defined in the claims. Obviously you can. In other words, the present invention should not be construed as being limited by the details of the specific examples and the accompanying drawings.
　本国際出願は、２０１７年３月８日に出願された日本国特許出願２０１７‐０４４３８５号に基づく優先権を主張するものであり、その全内容を本国際出願に援用する。 This international application claims priority based on Japanese Patent Application No. 2017-0434385 filed on March 8, 2017, the entire contents of which are incorporated herein by reference.
　１　　　　　靴
　２　　　　　計測デバイス
　２１　　　　センサ部
　２１１　　　基材
　２１２　　　圧力センサ
　２２　　　　通信部
　３　　　　　情報端末
　４　　　　　ネットワーク
　５　　　　　サーバ装置
　５０１　　　基本データ入力部
　５０２　　　計測データ受信部
　５０３　　　データ解析部
　５０４　　　立脚期データ解析部
　５０５　　　静止立位データ解析部
　５０６　　　ライフログ書込部
　５０７　　　評価部
　５０８　　　前足部局所荷重評価部
　５０９　　　回内足・回外足評価部
　５１０　　　複数歩バランス評価部
　５１１　　　立脚時間評価部
　５１２　　　両脚支持割合評価部
　５１３　　　左右差評価部
　５１４　　　足部アーチ評価部
　５１５　　　総合評価部
　５１６　　　総合分析部
　５１７　　　母趾接地評価部
　５２１　　　データベース
　５２２　　　ユーザデータ
　５２３　　　靴データ
　５２４　　　ライフログデータ
　５２５　　　位置データ
　５２６　　　計測データ
　５２７　　　歩行処理後データ
　５２８　　　静止立位処理後データ
　６　　　　　管理端末 DESCRIPTION OF SYMBOLS 1 Shoes 2 Measuring device 21 Sensor part 211 Base material 212 Pressure sensor 22 Communication part 3 Information terminal 4 Network 5 Server apparatus 501 Basic data input part 502 Measurement data receiving part 503 Data analysis part 504 Standing period data analysis part 505 Still standing data Analysis unit 506 Life log writing unit 507 Evaluation unit 508 Forefoot local load evaluation unit 509 Inward foot / extraction foot evaluation unit 510 Multi-step balance evaluation unit 511 Standing time evaluation unit 512 Both-leg support ratio evaluation unit 513 Right / left difference evaluation unit 514 Foot arch evaluation unit 515 Comprehensive evaluation unit 516 Comprehensive analysis unit 517 Toe contact evaluation unit 521 Database 522 User data 523 Shoe data 524 Life log data 525 Position data 526 Measurement data 527 Data after walking process 528 Data after stationary standing process 6 Management terminal

Claims (10)

  1. 　複数のユーザが使用する靴のインソールに設けられた１以上のセンサから歩行時および静止立位時における所定時間の少なくとも足底圧のデータを取得し、
    　取得されたデータを解析して、ユーザ毎の、歩行時における少なくとも足底圧パラメータ、足圧中心パラメータおよび時間パラメータと、静止立位時における少なくとも足底圧パラメータおよび足圧中心パラメータとを取得して蓄積する、
    処理をコンピュータが実行することを特徴とする歩行・足部評価方法。     Acquire at least plantar pressure data for a predetermined time during walking and standing from one or more sensors provided on an insole of shoes used by a plurality of users;
    Analyze the acquired data to obtain at least the plantar pressure parameter, the foot pressure center parameter, and the time parameter during walking, and at least the plantar pressure parameter and the foot pressure center parameter when standing still for each user. Accumulate,
    A walking / foot evaluation method characterized in that a computer executes processing.
  2. 　前記足底圧のデータは、靴に設けられた送信部から、直接に、または携帯端末を経由して、取得する、
    ことを特徴とする請求項１に記載の歩行・足部評価方法。     The plantar pressure data is acquired directly or via a mobile terminal from a transmitter provided in the shoe.
    The walking / foot evaluation method according to claim 1.
  3. 　歩行時における所定時間の足底圧のデータから、各センサの最大圧力値の複数歩の平均値を取得して歩行時における前記足底圧パラメータとし、足圧中心軌跡の屈曲角を取得して歩行時における前記足圧中心パラメータとし、立脚時間、両脚支持割合および単脚支持割合を取得して前記時間パラメータとし、
    　静止立位時における所定時間の足底圧のデータから、各センサの荷重比率を取得して静止立位時における前記足底圧パラメータとし、足圧中心総軌跡長および足圧中心矩形面積を取得して静止立位時における前記足圧中心パラメータとする、
    ことを特徴とする請求項１に記載の歩行・足部評価方法。     Obtain the average value of multiple steps of the maximum pressure value of each sensor from the plantar pressure data for a predetermined time during walking and use it as the plantar pressure parameter during walking to obtain the bending angle of the foot pressure center locus As the foot pressure center parameter at the time of walking, stand time, both leg support ratio and single leg support ratio are obtained as the time parameter,
    Acquire the load ratio of each sensor from the plantar pressure data for a predetermined time when standing still and use it as the plantar pressure parameter when standing still to obtain the total foot pressure center trajectory length and foot pressure center rectangular area. And the foot pressure center parameter when standing still,
    The walking / foot evaluation method according to claim 1.
  4. 　前記歩行時における所定時間の足底圧のデータから取得した足底圧パラメータ、足圧中心パラメータおよび時間パラメータから、胼胝・魚の目の評価を行って胼胝・魚の目評価結果を取得し、回内足・回外足の評価を行って回内足・回外足評価結果を取得し、複数歩バランスの評価を行って複数歩バランス評価結果を取得し、立脚時間の評価を行って立脚時間評価結果を取得し、両脚支持割合の評価を行って両脚支持割合評価結果を取得し、若しくは、母趾接地の評価を行って母趾接地の評価結果を取得し、
    　または、前記静止立位時における所定時間の足底圧のデータから取得した足底圧パラメータおよび足圧中心パラメータから、左右差の評価を行って左右差評価結果を取得し、若しくは、足部アーチの評価を行って足部アーチ評価結果を取得する、
    ことを特徴とする請求項１に記載の歩行・足部評価方法。     From the plantar pressure parameter, the foot pressure center parameter and the time parameter acquired from the plantar pressure data for a predetermined time at the time of walking, evaluate the eye of the carp / fish to obtain the eye evaluation result of the carp / fish, Evaluate the prosthetic foot, obtain the progenitor / extroverted foot evaluation result, evaluate the multi-step balance, obtain the multi-step balance evaluation result, evaluate the stance time, and obtain the stance time evaluation result Acquire and evaluate the support ratio of both legs to acquire the support ratio evaluation result of both legs, or evaluate the support of the toe and acquire the evaluation result of the toe contact,
    Alternatively, from the plantar pressure parameter and the foot pressure center parameter acquired from the plantar pressure data for a predetermined time when standing still, the left-right difference is evaluated and the left-right difference evaluation result is obtained, or the foot arch To obtain the foot arch evaluation result,
    The walking / foot evaluation method according to claim 1.
  5. 　前記胼胝・魚の目評価結果と前記回内足・回外足評価結果と前記足部アーチ評価結果と母趾接地の評価結果のうち、少なくとも１つを用いて美足度を取得し、
    　前記複数歩バランス評価結果と前記左右差評価結果とからカラダバランス度を取得し、
    　前記立脚時間評価結果と前記両脚支持割合評価結果とから歩き方リズムを取得し、
    　ユーザの行動を示すライフログから活動度を取得する、
    ことを特徴とする請求項４に記載の歩行・足部評価方法。     Obtaining a leg degree using at least one of the evaluation results of the eyelid and fish eye evaluation results, the pronation and supination foot evaluation results, the foot arch evaluation results and the heel contact,
    Obtain a body balance degree from the multi-step balance evaluation result and the left-right difference evaluation result,
    Obtain a walking rhythm from the stance time evaluation result and the both-leg support ratio evaluation result,
    Get activity from life log showing user behavior,
    The walking / foot evaluation method according to claim 4, wherein:
  6. 　前記美足度、前記カラダバランス度、前記歩き方リズムおよび前記活動度から、各々を独立した軸とする評価チャートを生成する、
    ことを特徴とする請求項５に記載の歩行・足部評価方法。     Generate an evaluation chart with each axis as an independent axis from the degree of beautiful legs, the degree of body balance, the rhythm of walking, and the degree of activity.
    The walking / foot evaluation method according to claim 5.
  7. 　前記胼胝・魚の目評価結果、前記回内足・回外足評価結果、前記母趾接地の評価結果、前記複数歩バランス評価結果、前記立脚時間評価結果、前記両脚支持割合評価結果、前記左右差評価結果および前記足部アーチ評価結果のうち、少なくとも１つの評価結果が類似し、かつ足部に異常の発生していない他のユーザを特定し、
    　当該他のユーザが使用している靴を選択候補として提示する、
    ことを特徴とする請求項４に記載の歩行・足部評価方法。     Eye evaluation result of the carp / fish, evaluation result of the prosthetic / extroverted foot, evaluation result of the toe contact, evaluation of the multi-step balance, evaluation of the stance time, evaluation result of the both legs support ratio, left-right difference evaluation Among the results and the foot arch evaluation results, identify other users who are similar in at least one evaluation result and have no abnormalities in the foot,
    Presenting shoes used by other users as selection candidates,
    The walking / foot evaluation method according to claim 4, wherein:
  8. 　蓄積された前記データおよび／または歩行時における少なくとも足底圧パラメータ、足圧中心パラメータ並びに時間パラメータと、静止立位時における少なくとも足底圧パラメータ並びに足圧中心パラメータのうち、少なくとも１つのパラメータに基づき、時間的な状態変化を加味して総合分析を行い、足部異常の早期発見または予測警告を行う、
    ことを特徴とする請求項１に記載の歩行・足部評価方法。     Based on the accumulated data and / or at least one of the foot pressure parameter, the foot pressure center parameter, and the time parameter during walking, and at least one of the foot pressure parameter and the foot pressure center parameter when standing still , Perform a comprehensive analysis taking into account temporal changes in the state, perform early detection or prediction warning of foot abnormalities,
    The walking / foot evaluation method according to claim 1.
  9. 　複数のユーザが使用する靴のインソールに設けられた１以上のセンサから歩行時および静止立位時における所定時間の少なくとも足底圧のデータを取得し、
    　取得されたデータを解析して、ユーザ毎の、歩行時における少なくとも足底圧パラメータ、足圧中心パラメータおよび時間パラメータと、静止立位時における少なくとも足底圧パラメータおよび足圧中心パラメータとを取得して蓄積する、
    処理をコンピュータに実行させることを特徴とする歩行・足部評価プログラム。     Acquire at least plantar pressure data for a predetermined time during walking and standing from one or more sensors provided on an insole of shoes used by a plurality of users;
    Analyze the acquired data to obtain at least the plantar pressure parameter, the foot pressure center parameter, and the time parameter during walking, and at least the plantar pressure parameter and the foot pressure center parameter when standing still for each user. Accumulate,
    A walking / foot evaluation program characterized by causing a computer to execute processing.
  10. 　複数のユーザが使用する靴のインソールに設けられた１以上のセンサから歩行時および静止立位時における所定時間の少なくとも足底圧のデータを取得するデータ取得部と、
    　取得されたデータを解析して、ユーザ毎の、歩行時における少なくとも足底圧パラメータ、足圧中心パラメータおよび時間パラメータと、静止立位時における少なくとも足底圧パラメータおよび足圧中心パラメータとを取得して蓄積する解析部と、
    を備えたことを特徴とする歩行・足部評価装置。     A data acquisition unit that acquires at least plantar pressure data for a predetermined time during walking and standing from one or more sensors provided on an insole of shoes used by a plurality of users;
    Analyze the acquired data to obtain at least the plantar pressure parameter, the foot pressure center parameter, and the time parameter during walking, and at least the plantar pressure parameter and the foot pressure center parameter when standing still for each user. An analysis unit that accumulates
    A walking / foot evaluation apparatus characterized by comprising:
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Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109528212A (en) * 2018-12-29 2019-03-29 大连乾函科技有限公司 A kind of abnormal gait identification device and method
WO2021140658A1 (en) * 2020-01-10 2021-07-15 日本電気株式会社 Anomaly detection device, determination system, anomaly detection method, and program recording medium
JP2021119935A (en) * 2020-01-31 2021-08-19 豊田合成株式会社 Walking analysis method and walking analysis device
CN113361342A (en) * 2021-05-20 2021-09-07 杭州麦淘淘科技有限公司 Multi-mode-based human body sitting posture detection method and device
JP2021137539A (en) * 2020-02-28 2021-09-16 株式会社三菱ケミカルホールディングス Measurement system, method, and program
JP2021137371A (en) * 2020-03-06 2021-09-16 豊田合成株式会社 Walking diagnostic system
WO2022038663A1 (en) * 2020-08-18 2022-02-24 日本電気株式会社 Detection device, detection system, detection method, and program recording medium
WO2022038664A1 (en) * 2020-08-18 2022-02-24 日本電気株式会社 Calculation device, gait measurement system, calculation method, and program recording medium
CN114120602A (en) * 2021-11-28 2022-03-01 王一山 Method and system for correcting posture of jumping ropes simultaneously by using big data
JP2022046214A (en) * 2020-09-10 2022-03-23 豊田合成株式会社 Walking information acquiring method, walking information acquiring device, and walking information acquisition system
WO2022070416A1 (en) * 2020-10-02 2022-04-07 日本電気株式会社 Estimation device, estimation method, and program recording medium
WO2022239120A1 (en) * 2021-05-11 2022-11-17 日本電信電話株式会社 Fall prediction device, fall prediction program, and fall prediction method
JP7395336B2 (en) 2019-11-18 2023-12-11 財團法人資訊工業策進會 Foot pressure measuring device, method, and non-transitory tangible machine-readable medium thereof
US11925483B2 (en) 2020-07-03 2024-03-12 Nec Corporation Risk estimation apparatus, risk estimation system, risk estimation method, and non-transitory computer readable recording medium

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004141275A (en) * 2002-10-22 2004-05-20 Japan Science & Technology Agency Sole pressure distribution - auditory biofeedback system
JP2008256470A (en) * 2007-04-03 2008-10-23 Nitta Ind Corp Pressure distribution sensor system
JP2009106391A (en) * 2007-10-26 2009-05-21 Panasonic Electric Works Co Ltd Gait analyzing system
JP2009106545A (en) * 2007-10-30 2009-05-21 Univ Of Occupational & Environmental Health Japan Sole pressure measuring instrument and method for determination of behavior/posture
US20160324445A1 (en) * 2015-05-07 2016-11-10 Samsung Electronics Co., Ltd. Method of providing information according to gait posture and electronic device for same

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005242499A (en) 2004-02-25 2005-09-08 Sanyo Electric Co Ltd Communication system and information processing method
JP4863365B2 (en) * 2006-05-31 2012-01-25 アニマ株式会社 Motion analysis system, motion analysis device, and program
JP4805070B2 (en) * 2006-09-07 2011-11-02 ニッタ株式会社 Fall risk assessment device
JP5362962B2 (en) 2007-03-29 2013-12-11 株式会社Ｃｏｍｆｏｒｔ Ｌａｂ Shoe or insole fitting navigation system
JP5886086B2 (en) 2012-03-06 2016-03-16 テルモ株式会社 Sole pressure measuring device and processing method thereof
JP2013192721A (en) * 2012-03-19 2013-09-30 Terumo Corp Foot pressure distribution measurement system and information processor
JP6448015B2 (en) * 2014-04-23 2019-01-09 株式会社タニタ Motor function measuring device, method, and program
JP2016123816A (en) 2015-01-08 2016-07-11 セイコーエプソン株式会社 Biological information analysis device, biological information analysis system and biological information analysis program

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004141275A (en) * 2002-10-22 2004-05-20 Japan Science & Technology Agency Sole pressure distribution - auditory biofeedback system
JP2008256470A (en) * 2007-04-03 2008-10-23 Nitta Ind Corp Pressure distribution sensor system
JP2009106391A (en) * 2007-10-26 2009-05-21 Panasonic Electric Works Co Ltd Gait analyzing system
JP2009106545A (en) * 2007-10-30 2009-05-21 Univ Of Occupational & Environmental Health Japan Sole pressure measuring instrument and method for determination of behavior/posture
US20160324445A1 (en) * 2015-05-07 2016-11-10 Samsung Electronics Co., Ltd. Method of providing information according to gait posture and electronic device for same

Cited By (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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CN109528212B (en) * 2018-12-29 2023-09-19 大连乾函科技有限公司 Abnormal gait recognition equipment and method
JP7395336B2 (en) 2019-11-18 2023-12-11 財團法人資訊工業策進會 Foot pressure measuring device, method, and non-transitory tangible machine-readable medium thereof
WO2021140658A1 (en) * 2020-01-10 2021-07-15 日本電気株式会社 Anomaly detection device, determination system, anomaly detection method, and program recording medium
JPWO2021140658A1 (en) * 2020-01-10 2021-07-15
JP7327516B2 (en) 2020-01-10 2023-08-16 日本電気株式会社 Abnormality detection device, judgment system, abnormality detection method, and program
JP7188401B2 (en) 2020-01-31 2022-12-13 豊田合成株式会社 Gait analysis method and gait analysis device
JP2021119935A (en) * 2020-01-31 2021-08-19 豊田合成株式会社 Walking analysis method and walking analysis device
JP7419616B2 (en) 2020-02-28 2024-01-23 株式会社Shosabi Measurement systems, methods and programs
JP2021137539A (en) * 2020-02-28 2021-09-16 株式会社三菱ケミカルホールディングス Measurement system, method, and program
JP2021137371A (en) * 2020-03-06 2021-09-16 豊田合成株式会社 Walking diagnostic system
JP7283422B2 (en) 2020-03-06 2023-05-30 豊田合成株式会社 Gait diagnosis system
US11925483B2 (en) 2020-07-03 2024-03-12 Nec Corporation Risk estimation apparatus, risk estimation system, risk estimation method, and non-transitory computer readable recording medium
WO2022038664A1 (en) * 2020-08-18 2022-02-24 日本電気株式会社 Calculation device, gait measurement system, calculation method, and program recording medium
JP7480851B2 (en) 2020-08-18 2024-05-10 日本電気株式会社 DETECTION APPARATUS, DETECTION SYSTEM, DETECTION METHOD, AND PROGRAM
JP7480852B2 (en) 2020-08-18 2024-05-10 日本電気株式会社 Calculation device, gait measurement system, calculation method, and program
WO2022038663A1 (en) * 2020-08-18 2022-02-24 日本電気株式会社 Detection device, detection system, detection method, and program recording medium
JP7331810B2 (en) 2020-09-10 2023-08-23 豊田合成株式会社 Walking information acquisition method, walking information acquiring device, and walking information acquiring system
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