CN114366466A - Walking-replacing nursing robot integrating health information monitoring and prediction - Google Patents

Walking-replacing nursing robot integrating health information monitoring and prediction Download PDF

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CN114366466A
CN114366466A CN202111505360.3A CN202111505360A CN114366466A CN 114366466 A CN114366466 A CN 114366466A CN 202111505360 A CN202111505360 A CN 202111505360A CN 114366466 A CN114366466 A CN 114366466A
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CN114366466B (en
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王冲
黄圣
马滕
茅健
赖磊捷
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Shanghai University of Engineering Science
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    • A—HUMAN NECESSITIES
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    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
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    • A—HUMAN NECESSITIES
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    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
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    • A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
    • A—HUMAN NECESSITIES
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    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G5/00—Chairs or personal conveyances specially adapted for patients or disabled persons, e.g. wheelchairs
    • A61G5/10—Parts, details or accessories
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G5/00—Chairs or personal conveyances specially adapted for patients or disabled persons, e.g. wheelchairs
    • A61G5/10—Parts, details or accessories
    • A61G5/1051—Arrangements for steering
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
    • A61G2203/00—General characteristics of devices
    • A61G2203/10—General characteristics of devices characterised by specific control means, e.g. for adjustment or steering
    • A61G2203/20—Displays or monitors

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Abstract

The invention provides a mobility assistance nursing robot integrating health information monitoring and prediction, and belongs to the technical field of medical nursing equipment. This equipment includes the wheelchair body, its characterized in that: the device comprises a detection unit, wherein the detection unit comprises a display module, a detection box and a control box; the display module is used for controlling and displaying monitoring information; the detection box comprises an integrated cuff type blood pressure monitoring device, a heart rate detection module and a blood oxygen detection module; the control box comprises a control unit and a data processing unit, can realize motion control and storage and data analysis of detection information, and establishes health files and uploads the health files through a mobile phone. The invention can effectively solve the problem of mobility of the elderly disabled population, enhances the living satisfaction, stores the data of heart rate, blood pressure, blood oxygen and the like monitored in daily life, builds the personal health information file of the user by depending on the analysis of a big data interpolation algorithm, and can help the doctor to correctly master the daily physical condition of the patient.

Description

Walking-replacing nursing robot integrating health information monitoring and prediction
Technical Field
The invention belongs to the technical field of medical care equipment, and particularly relates to a mobility-assisted care robot integrating health information monitoring and prediction.
Background
With the development of economic society, the problem of population aging in the global scope is increasingly serious, for example, China is taken as an example, the population aged 2020 is increased to 2.64 hundred million people, and the percentage of the population aged is increased to 18.7 percent of the total population. And the incidence and disability rate of cardiovascular and cerebrovascular diseases, orthopedic diseases and the like in the elderly population are increased, and the pressure for the rehabilitation and the care of disabled elderly is great. According to the Beizhess consultation data, nearly 24% of residents in China do not know rehabilitation treatment, only 26% of residents have correct cognition on rehabilitation treatment, and the data show that the consumption of per capita rehabilitation treatment in China in 2017 is 5.5 dollars, which is far lower than the consumption of per capita rehabilitation treatment in the United states in the same year by 54 dollars, so that the potential of mining in China at the demand side is very large. Therefore, it is necessary to design a rehabilitation nursing robot which integrates the monitoring of health information such as heart rate, blood pressure and blood oxygen, simultaneously stores and analyzes data to form a health file, and has the functions of household walking and pressure relief for nursing staff.
Health monitoring accompanying robot that appears in the existing market mainly focuses on man-machine interaction, mainly focus on the condition that lacks the accompanying to the old person and appear the health disease or can't obtain in time treatment when appearing danger, consequently mainly utilize acceleration sensor monitoring old person whether fall down etc. the man-machine interaction function is subject to the restriction of current technology, it is relatively poor in speech recognition and actual question and answer experience, health monitoring mainly relies on the intelligent bracelet of wearing, fail to consider that the bracelet is subject to the shape size and wears the requirement and lead to the detection accuracy limited. At present, the heart rate is detected by the intelligent bracelet mostly by adopting a PPG photoplethysmography principle, the pulse is measured in an optical mode, the improvement of the test experiment for many years is mature, but the detection of the blood pressure by the bracelet cannot be accurate, and the blood pressure is judged to be unqualified when the error exceeds 4mmHG according to the regulation of JJG 692-2010 noninvasive automatic measuring sphygmomanometer verification regulations, so that if the detected blood pressure is not consistent with the reality, the dosage of a hypertensive is probably influenced, and the health is influenced. Secondly, the existing health monitoring accompanying robot mainly focuses on the early warning function, fails to store and analyze the monitored data, and misses the opportunity of forming the health file of the user, so it is necessary to improve and design the health monitoring accompanying robot in view of these problems.
Disclosure of Invention
Aiming at the technical problems, the invention provides a mobility-assisted nursing robot integrating health information monitoring and prediction, which can effectively solve the problems that the aged people are incapacitated, cannot walk independently and has high dependence on nursing staff; on the other hand, the problem that the discovery time is late and doctors cannot obtain the daily health information of the patients due to the fact that diseases such as heart and cerebral vessels depend on health physical examination can also be solved.
In order to achieve the purpose, the invention adopts the technical scheme that: the utility model provides an integrated health information monitoring and nursing robot of riding instead of walk of prediction, includes the wheelchair main part, its characterized in that: the device comprises a detection unit, wherein the detection unit comprises a display module, a detection box and a control box; the display module is used for controlling and displaying monitoring information; the detection box is positioned below the armrests of the wheelchair main body, and comprises an integrated cuff type blood pressure monitoring device, a heart rate detection module and a blood oxygen detection module inside; the blood pressure monitoring device adopts an electronic sphygmomanometer; the heart rate detection module detects the heart rate through a receiver arranged on the cuff; the blood oxygen monitoring adopts a reflection type blood oxygen sensor chip, and the saturation of the blood oxygen is calculated through the difference of the emitted light intensity and the received light intensity; the control box is positioned below a seat of the wheelchair main body and comprises a control unit and a data processing unit, the control unit integrates an Ardnino module and a Bluetooth module, can be matched with a smart phone, and realizes mobile control of the nursing robot based on a mobile phone platform by downloading an APP through the smart phone, an APP control interface can realize the functions of advancing, retreating, turning left, turning right and motor speed regulation, and meanwhile prompts can be set for health monitoring; the data processing unit stores and analyzes the received information, and uploads the data to the mobile phone through the control unit to establish a health file.
Further, the walking-replacing nursing robot comprises a motion unit, wherein the motion unit comprises a driving motor, a control rocker and a battery pack; the control rocker has multiple gears to select and adjust the moving speed, so that the control rocker is convenient for old disabled people to use; the battery pack supplies power to the robot.
Furthermore, the control rocker adopts a 24V universal rocker intelligent controller with an EABS slope-stopping function, the human-computer interface is provided with an LED prompt, the driving mode is PWM duty ratio adjustment and electronic differential, and the control rocker has a controller alarm sound. The battery pack adopts a lithium battery pack with nominal capacity of 40Ah and standard voltage DC24V, and can ensure that the maximum one-time charging stroke is about 6 kilometers; the charger has the input voltage of 220V and 50HZ, the output voltage of 29.2V and the output current of 5A.
Further, the control box adopts STM32F103RCT6 series control chips.
Furthermore, the display module comprises a measurement information display screen, an indicator light and an operation key, and is used for measurement control, information display and alarm prompt.
The invention also provides a method for integrated health information detection and prediction, which is a walking-instead nursing robot based on the integrated health information monitoring and prediction, and is characterized by comprising the following steps:
(1) utilize the nursing robot of riding instead of walk carries out the monitoring of blood pressure, rhythm of the heart and blood oxygen:
and (3) blood pressure detection: the electronic sphygmomanometer is adopted, after a user takes the cuff out of the detection box, the air insertion nozzle rubber tube is connected, and the blood pressure detection operation is carried out through the keys on the display module;
heart rate detection: green light is emitted through a photoelectric receiver arranged on the cuff; after the light beam is emitted, skin, muscle tissue and blood can absorb a part of the light beam, the rest light beam is reflected to the receiver, the heart can generate reflected light with different colors during contraction and relaxation, and the received electric signal also changes along with the pulse; the signal is demodulated through an algorithm, and the heart rate is converted;
blood oxygen monitoring: a reflective blood oxygen sensor chip is adopted, two light emitting diodes are arranged in the chip, red light with the wavelength of 669nm and infrared light with the wavelength of 880nm are emitted to the wrist part respectively, the photodiode on the other side is used for receiving reflected light, and the saturation of blood oxygen is calculated through the difference between the emitted light intensity and the received light intensity;
(2) the robot is used for storing monitoring data, all detected information is transmitted to the control box, the data processing unit stores the data, and the information comprises the types of the detected data, such as blood pressure, heart rate and blood oxygen; secondly, detecting time information, specifically day; and finally, judging whether the data value is in a normal interval, specifically a blood pressure value: systolic pressure is 90-140mmHg, diastolic pressure is 60-90mmHg, heart rate is 60-100 times/min, blood oxygen concentration is more than 90%, and automatic prompting is performed through the display module when the set time limit is exceeded;
(3) the walking robot is used for carrying out data processing on the stored data, so that the smoothness of a multi-parameter health record curve of a user is ensured, and the health condition of the user can be predicted;
(4) and storing all results and uploading the results to the mobile phone through the control module to establish a health file.
Further, the data processing step in step (3) is as follows:
(3.1) after the measured data exceeds one week, preprocessing a plurality of groups of data: arithmetic mean of a quantity:
Figure BDA0003404079970000051
the standard deviation of the measurements is given according to the bessel formula:
Figure BDA0003404079970000052
is the residual error;
and (3.2) judging whether the processed data information has a coarse error according to a 3 sigma criterion, eliminating coarse error data, and reserving residual data.
(3.3) using an interpolation algorithm for the data;
after a plurality of groups of measured data are cleaned and filtered, data are encrypted between two times of measured data based on a Markov theory, the number of times of overall encryption is two, and the value taking point of initial encryption is Bn, so that the following formula is satisfied:
Figure BDA0003404079970000053
the secondary densification value taking points are Cn and Dn, wherein the position of Cn is the middle side with equal spacing between An and Bn, the position of Dn is the middle side with equal spacing between Bn and An +1, the whole data volume is 3 times of the original data volume after twice densification, and the calculation formula is as follows:
Figure BDA0003404079970000061
substitution Bn denotes the function can be:
Cn＝KAn+(i*k+j)An-1+i2An-2+i*jAn-3
the formula of Dn is calculated in the same way as follows:
Figure BDA0003404079970000062
substitution Bn denotes the function can be:
Dn＝(K2+i)An+k(i+j)An-1+j(k+i)An-2+j2An-3
considering the smoothness of the fitting curve, adopting quadratic polynomial interpolation, and performing function fitting by using the nodes to obtain:
Figure BDA0003404079970000063
on the basis of enhancing the reliability of data by fitting the quadratic function, the method can realize a prediction function, and can realize early warning of health diseases when the predicted value is obviously deviated from a normal value.
Further, to obtain a more accurate interpolation result, the three interpolation coefficients are preferably set as: k is 0.5, i is 0.3, and j is 0.2.
Drawings
Fig. 1 is a schematic structural view of a nursing robot according to the present invention.
FIG. 2 is a schematic view of the right control rocker of the present invention.
FIG. 3 is a schematic view of the left hand side detection box of the present invention.
FIG. 4 is a schematic view of the bottom control unit of the present invention.
FIG. 5 is a schematic diagram of the primary densification of the present invention.
FIG. 6 is a schematic diagram of multi-weight interpolation and prediction according to the present invention.
Wherein: the multifunctional seat cushion comprises a headrest 1, a display module 2, a detection box 3, a control box 4, a battery pack 5, a rubber rear wheel 6, a driving motor 7, a seat frame unit 8, a pedal 9, an omnidirectional wheel 10, a leg cushion 11, a seat cushion 12, a control rocker 13, a soft handrail 14 and a seat back cushion 15.
Detailed Description
The invention is further described below with reference to the figures and examples.
The walking-instead nursing robot integrating health information monitoring and prediction is designed at this time (as shown in fig. 1): the overall structure comprises three parts, namely a seat frame body, a motion unit and a detection unit, wherein the seat frame body meets the actual riding requirement and comprises a headrest 1, a rear wheel rubber wheel 6, a seat frame unit 8, pedals 9, front wheel omni-wheels 10, leg pads 11, a seat cushion 12, soft armrests 14 and a seat back pad 15. The moving unit consists of a battery pack 5, a driving motor 7 and a control rocker 13, wherein the turning radius of the front wheel omni-directional wheel 10 is small, the transverse movement damping is low, the indoor use is suitable, the control rocker is convenient for old disabled people to use, the moving speed is adjustable, the indoor moving speed can be adjusted to 1 gear, and the outdoor moving speed can be adjusted to 5 gears to accelerate the moving speed. The exercise system can effectively replace the problem that the elderly and disabled people depend on the actions of nursing staff, and the nursing pressure is reduced. The detection unit comprises display module 2, detection case 3 and control box 4, and detection case 3 can conveniently be opened, and inside integrated sleeve belt formula blood pressure test device and heart rate blood oxygen monitoring, display module 2 have measurement information display screen and pilot lamp and operation button.
The materials selected in the design should meet halogen-free requirements and meet ROHS certification standards. The main structure part of the seat frame unit is formed by welding 6061 aluminum profiles, and the seat frame unit is good in cold processing performance, high in corrosion resistance and toughness and convenient for subsequent coloring films. The front wheel arm, the front beam, the plug, the screw mounting block, the bearing sleeve and the like are all made of 6061 aluminum alloy materials; the rotating front wheel shaft adopts a chrome-plated smooth circle, so that the rotating front wheel shaft is high in hardness, wear-resistant and corrosion-resistant; the pedal mounting plate, the spacer bush, the bearing retainer ring, the shaft end retainer ring and the pedal push seat are all made of stainless steel 304; the main bearing seat plate, the bottom support and the rear wheel support welding group are made of Q235-A carbon steel, so that the comprehensive performance is better; the pedal 9 is made of plastic, so that the pedal is simple and durable; the leg pads 11, seat pads 12, back pads 15 and soft armrests 14 are custom made, leather exterior, ensuring comfort.
The right control rocker 13 adopts a 24V universal rocker intelligent controller with an EABS slope-stopping function, the human-computer interface is provided with an LED prompt, the driving mode is PWM duty ratio adjustment and electronic differential, and the controller gives an alarm.
The left side is detection case 3, and detection case 3 can conveniently be opened, and inside cuff formula blood pressure check device and heart rate blood oxygen monitoring module of being equipped with, and blood pressure check adopts the higher electron sphygmomanometer of the accuracy performance that the hospital used, and the user takes out the back with the cuff from detection case 3, connects the air faucet rubber tube, carries out the blood pressure check operation through the button on the display module 2, and the test result also can show on the display screen. The principle is that the oscillometric method indirectly measures the blood pressure, the pulse wave information is sensed in the process of the deflation or inflation of the cuff, and the blood pressure data is obtained through a series of complex conversion and calculation. The heart rate detection adopts the principle of PPG photoplethysmography, and emits green light through a photoelectric receiver arranged on the cuff. After the light beam is emitted, a part of the skin, muscle tissue and blood is absorbed, and the rest is reflected back to the receiver. The reflection of structures such as bones, skin, fat and the like of a human body to light is a fixed value, and the capillary vessels and the arteriovenous vessels change along with the pulsation of the heart pulse, so that the reflection value changes continuously and fluctuatively, and blood tends to reflect red light and absorb green light, so that the heart can generate reflected light with different colors during contraction and relaxation, which is reflected in that a received electric signal at a receiving end also changes along with the pulse. The maximum light absorption of the peripheral blood volume is also the greatest and the detected light intensity is the smallest when the heart contracts. When the heart is in diastole, on the contrary, the detected light intensity is the maximum, so that the light intensity received by the light receiver is in a pulsating change. The signal can be demodulated through an algorithm, and then the heart rate is converted by applying a certain algorithm. The blood oxygen monitoring adopts a reflection type blood oxygen sensor chip, two light emitting diodes are arranged in the chip, red light with the wavelength of 669nm and infrared light with the wavelength of 880nm are respectively emitted to the wrist, the photodiode on the other side is used for receiving reflected light, and the saturation of the blood oxygen can be calculated through the difference between the emitted light intensity and the received light intensity. All detected information is transferred to a storage unit in the control box 3 to await subsequent processing.
The battery pack 5 and the control box 4 are arranged under the seat, wherein the battery pack 5 adopts a lithium battery pack with the nominal capacity of 40Ah and the standard voltage of DC24V, and the maximum charging stroke of one time can be ensured to be about 6 kilometers. The input voltage of the charger is 220V, the output voltage of the charger is 50HZ, and the output voltage of the charger is 29.2V. The output current was 5A.
The control chip in the control box 4 adopts STM32F103RCT6 series, has high performance, low cost and low power consumption, and mainly realizes the functions of controlling the forward movement, backward movement, left-right rotation, opening and closing of the detection module and the like. The data detected each time can be summarized to the module, so that subsequent calling is facilitated. The control box 4 also comprises a data processing unit, which firstly stores data, the information includes the type of detected data, such as blood pressure, heart rate and blood oxygen. And secondly, detecting time information, namely, the time information is detected by days. And finally, the data value is the size, and the system can automatically give an alarm to prompt when the data value exceeds a normal set interval, specifically the blood pressure value: systolic pressure is 90-140mmHg, diastolic pressure is 60-90 mmHg. The heart rate is 60-100 times/min. The blood oxygen concentration is more than 90 percent. When the set time limit is exceeded, the machine can automatically prompt, and a user can consider to measure again or measure repeatedly in a time interval, so that the result is accurate.
Data processing stageAnd processing the multiple groups of data after the measured data exceeds one week. A series of equal precision measurements are made on a quantity, the measured values are all different due to random errors, and the arithmetic mean of all the measured values is taken as the final measurement result. Let l1，l2，…,lnIs the value obtained by n measurements, then the arithmetic mean value
Figure BDA0003404079970000101
The arithmetic mean value is closest to the true value, and is known from the law of probability theory of large numbers, and if the number of measurements increases infinitely, the arithmetic mean value
Figure BDA0003404079970000102
Inevitably approaches to true value L0. Wherein
Figure BDA0003404079970000103
Referred to as residual error. Considering the measurement as an equal precision measurement, the standard deviation of the measurement is obtained according to the Bessel equation:
Figure BDA0003404079970000104
to convert to:
Figure BDA0003404079970000105
and judging whether the processed data information has a coarse error according to a 3 sigma criterion, eliminating data which may have the coarse error, and reserving residual data.
Based on different weight coefficient ratio influences of multiple measurement values, after multiple groups of measured data are cleaned and filtered, the influence of near-three measured data of a user on subsequent results is comprehensively considered on the basis of a Markov theory, so that data encryption is performed between two times of measured data, the overall encryption frequency is twice, the data volume can be increased to 3 times of the original data volume, a primary encryption schematic diagram is shown in FIG. 5, and a encryption value taking point is Bn, and the following formula is satisfied:
Figure BDA0003404079970000106
the points of the second densification are Cn and Dn. The Cn is located at the middle side with equal spacing between An and Bn, the Dn is located at the middle side with equal spacing between Bn and An +1, the whole data volume is 3 times of the original data volume after twice densification, and the calculation formula is as follows:
Figure BDA0003404079970000111
substitution Bn denotes the function can be:
Cn＝KAn+(i*k+j)An-1+i2An-2+i*jAn-3
the formula of Dn is calculated in the same way as follows:
Figure BDA0003404079970000112
substitution Bn denotes the function can be:
Dn＝(K2+i)An+k(i+j)An-1+j(k+i)An-2+j2An-3
considering the smoothness of the fitting curve, adopting quadratic polynomial interpolation, and performing function fitting by using the nodes to obtain:
Figure BDA0003404079970000113
as shown in fig. 6, on the basis of enhancing the reliability of data by fitting the quadratic function, the prediction function can be realized, and early warning of health diseases can be realized when the predicted value is obviously deviated from the normal value.
Comprehensively considering different weight coefficients of the health information measured for multiple times, and through multiple experimental tests, in order to obtain a more accurate interpolation result, setting the three coefficients as: k is 0.5, i is 0.3, and j is 0.2.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.

Claims (9)

1. The utility model provides an integrated health information monitoring and nursing robot of riding instead of walk of prediction, includes the wheelchair main part, its characterized in that: the device comprises a detection unit, wherein the detection unit comprises a display module (2), a detection box (3) and a control box (4);
the display module (2) is used for controlling and displaying monitoring information;
the detection box (3) is positioned below the armrests of the wheelchair main body, and comprises an integrated cuff type blood pressure monitoring device, a heart rate detection module and a blood oxygen detection module inside; the blood pressure monitoring device adopts an electronic sphygmomanometer; the heart rate detection module detects the heart rate through a photoelectric sensor arranged on the cuff; the blood oxygen monitoring adopts a reflection type blood oxygen sensor chip, and the saturation of the blood oxygen is calculated through the difference of the emitted light intensity and the received light intensity;
the control box (4) is located below a seat of the wheelchair main body and comprises a control unit and a data processing unit, the control unit integrates an Ardnino module and a Bluetooth module, can be matched with a smart phone, and realizes mobile control of the nursing robot based on a mobile phone platform by downloading an APP through the smart phone, an APP control interface can realize the functions of advancing, retreating, turning left, turning right and motor speed regulation, and meanwhile, prompts can be set for health monitoring; the data processing unit stores and analyzes the received information, and uploads the data to the mobile phone through the control unit to establish a health file.
2. The integrated health information monitoring and prognostics walking-on-behalf-care robot of claim 1, characterized in that it comprises a motion unit comprising a drive motor (7), a control rocker (13), and a battery pack (5); the control rocker (13) has multiple gears to select and adjust the moving speed, so that the multifunctional electric blanket is convenient for old disabled people to use; the battery pack (5) supplies power to the robot.
3. The integrated health information monitoring and predicting mobility care robot according to claim 2, wherein the control rocker (13) adopts a 24V universal rocker intelligent controller with an EABS hill-holding function, the human-computer interface is provided with an LED prompt, the driving mode is PWM duty ratio adjustment and electronic differential speed, and the controller alarm sound is provided.
4. The integrated health information monitoring and forecasting mobility care robot according to claim 2, characterized in that the battery pack (5) adopts a lithium battery pack with a nominal capacity of 40Ah and a standard voltage DC24V, and can ensure a maximum charging stroke of about 6 km per time; the charger has the input voltage of 220V and 50HZ, the output voltage of 29.2V and the output current of 5A.
5. The integrated health information monitoring and prognostics walk-substituting care robot of claim 1, characterized in that the control box (4) employs a STM32F103RCT6 series control chip.
6. The integrated health information monitoring and prognostics walk-substituting care robot of claim 1, characterized in that the display module (2) comprises a measurement information display screen, indicator lights and operation keys for measurement control, information display and alarm prompting.
7. A method for integrated health information detection and prediction based on the integrated health information monitoring and prediction walk-by care robot as claimed in any one of claims 1 to 6, characterized by comprising the steps of:
(1) utilize the nursing robot of riding instead of walk carries out the monitoring of blood pressure, rhythm of the heart and blood oxygen:
and (3) blood pressure detection: by adopting the electronic sphygmomanometer, after a user takes the cuff out of the detection box (3), the air insertion nozzle rubber tube is connected, and the blood pressure detection operation is carried out through the keys on the display module (2);
heart rate detection: green light is emitted through a photoelectric receiver arranged on the cuff; after the light beam is emitted, skin, muscle tissue and blood can absorb a part of the light beam, the rest light beam is reflected to the receiver, the heart can generate reflected light with different colors during contraction and relaxation, and the received electric signal also changes along with the pulse; the signal is demodulated through an algorithm, and the heart rate is converted;
blood oxygen monitoring: a reflective blood oxygen sensor chip is adopted, two light emitting diodes are arranged in the chip, red light with the wavelength of 669nm and infrared light with the wavelength of 880nm are emitted to the wrist part respectively, the photodiode on the other side is used for receiving reflected light, and the saturation of blood oxygen is calculated through the difference between the emitted light intensity and the received light intensity;
(2) the walking robot is used for storing monitoring data, all detected information is transmitted to the control box (4), the data processing unit stores the data, and the information comprises the types of the detected data, such as blood pressure, heart rate and blood oxygen; secondly, detecting time information, specifically day; and finally, judging whether the data value is in a normal interval, specifically a blood pressure value: systolic pressure is 90-140mmHg, diastolic pressure is 60-90mmHg, heart rate is 60-100 times/min, blood oxygen concentration is more than 90%, and automatic prompting is performed through the display module (2) when the set time limit is exceeded;
(3) the walking robot is used for carrying out data processing on the stored data, the smoothness of a multi-parameter health record curve of a user is guaranteed by adopting a multi-weight interpolation algorithm, and the health condition of the user can be predicted;
(4) and storing all results, uploading the results to the mobile phone through the control module, and establishing a health file.
8. The method of claim 7, wherein the data processing step in step (3) is as follows:
(3.1) after the measured data exceeds one week, preprocessing a plurality of groups of data: arithmetic mean of a quantity:
Figure FDA0003404079960000041
the standard deviation of the measurements is given according to the bessel formula:
Figure FDA0003404079960000042
wherein
Figure FDA0003404079960000043
Is the residual error;
(3.2) judging whether the processed data information has a coarse error according to a 3 sigma criterion, eliminating coarse error data, and reserving residual data;
(3.3) using an interpolation algorithm for the data;
after a plurality of groups of measured data are cleaned and filtered, data are encrypted between two times of measured data based on a Markov theory, the number of times of overall encryption is two, and the value taking point of initial encryption is Bn, so that the following formula is satisfied:
Figure FDA0003404079960000044
the secondary densification value taking points are Cn and Dn, wherein the position of Cn is the middle side with equal spacing between An and Bn, the position of Dn is the middle side with equal spacing between Bn and An +1, the whole data volume is 3 times of the original data volume after twice densification, and the calculation formula is as follows:
Figure FDA0003404079960000045
substitution Bn denotes the function can be:
Cn＝KAn+(i*k+j)An-1+i2An-2+i*jAn-3
the formula of Dn is calculated in the same way as follows:
Figure FDA0003404079960000051
substitution Bn denotes the function can be:
Dn＝(K2+i)An+k(i+j)An-1+j(k+i)An-2+j2An-3
considering the smoothness of the fitting curve, adopting quadratic polynomial interpolation, and performing function fitting by using the nodes to obtain:
Figure FDA0003404079960000052
on the basis of enhancing the reliability of data by fitting the quadratic function, the method can realize a prediction function, and can realize early warning of health diseases when the predicted value is obviously deviated from a normal value.
9. The method of claim 8, wherein to obtain a more accurate interpolation result according to the health information rule, the three interpolation coefficients are preferably set as: k is 0.5, i is 0.3, and j is 0.2.
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