CN110974232A - Wearable load monitoring and rehabilitation training intelligent auxiliary device - Google Patents
Wearable load monitoring and rehabilitation training intelligent auxiliary device Download PDFInfo
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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
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/1036—Measuring load distribution, e.g. podologic studies
- A61B5/1038—Measuring plantar pressure during gait
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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
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
- A61B5/6804—Garments; Clothes
- A61B5/6807—Footwear
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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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- 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
- A61B5/7455—Details of notification to user or communication with user or patient ; user input means characterised by tactile indication, e.g. vibration or electrical stimulation
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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
- A61B5/74—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
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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
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/7465—Arrangements for interactive communication between patient and care services, e.g. by using a telephone network
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Abstract
A wearable load monitoring and rehabilitation training intelligent auxiliary device comprises: flexible sole pressure measurement shoe-pad, data acquisition module, vibrations feedback module and built-in low limbs bear a burden the intelligent bracelet of estimating the model, wherein: the data acquisition circuit receives pressure data of each region of the sole measured by a film type pressure sensor in the flexible sole pressure measuring insole and respectively sends the pressure data to the intelligent bracelet and the vibration feedback module in a wireless mode; the intelligent bracelet calculates and displays the data of the load of the lower limbs of the human body in real time according to the lower limb load estimation model and the pressure data; when the human lower limb load data from the intelligent bracelet exceeds the reasonable load range, the vibration module connected with the flexible sole pressure measurement insole sends out a vibration prompt.
Description
Technical Field
The invention relates to the technology in the field of medical appliances, in particular to a wearable intelligent auxiliary device for load monitoring and rehabilitation training.
Background
For orthopedic lower limb postoperative patients, reasonable and effective weight training can promote bone healing and limb function recovery. In order to prevent the secondary injury, the load of the lower limbs of the patient cannot exceed a reasonable range. In order to obtain good training effect, the load of the lower limbs of the patient cannot be too low. However, in actual weight training, due to lack of effective monitoring means, it is difficult for patients to follow reasonable weight-bearing strength given by doctors, which affects rehabilitation effect and at the same time, safety is difficult to guarantee.
Disclosure of Invention
The invention provides a wearable intelligent auxiliary device for load monitoring and rehabilitation training, which is used for lower limb load monitoring and rehabilitation training of orthopedic postoperative patients and aims to solve the problems that the existing lower limb load monitoring device is not perfect in design and difficult to give consideration to accuracy and comfort.
The invention is realized by the following technical scheme:
the invention comprises the following steps: flexible sole pressure measurement shoe-pad, data acquisition module, vibrations feedback module and built-in intelligent bracelet based on the low limbs heavy burden estimation model of many linear regression, wherein: the data acquisition circuit receives pressure data of each region of the sole measured by a film type pressure sensor in the flexible sole pressure measuring insole and respectively sends the pressure data to the intelligent bracelet and the vibration feedback module in a wireless mode; the intelligent bracelet calculates and displays the data of the load of the lower limbs of the human body in real time according to the lower limb load estimation model and the pressure data; when the human lower limbs heavy burden data that comes from intelligent bracelet surpassed reasonable heavy burden scope, send the vibrations suggestion with the vibrations module that flexible plantar pressure measurement shoe-pad is connected through the bluetooth.
The flexible plantar pressure measuring insole comprises: flexible area is decided and shoe-pad, film formula pressure sensor and the plastic cover that from top to bottom sets gradually, wherein: the flexible shaping belt is respectively connected with the plastic sleeve and the vibration module.
The thin film type pressure sensor is a piezoresistive flexible sole pressure sensor, 8-10 pressure sensitive units respectively measure the stress of a plurality of local areas of the sole, and the pressure sensitive units specifically comprise: the shell, the strain resistor arranged in the shell and the lead wire connected with the strain resistor.
The data acquisition module and the vibration feedback module are arranged in a flexible binding band connected with the flexible sole pressure measuring insole so as to adjust the binding position and the tightness degree according to the injury condition of the legs and combine with the flexible sole pressure measuring insole.
The lower limb load estimation model is established based on sample data, preferably is a multiple linear regression model, is established by collecting a large amount of sample data through experiments, and estimates the total lower limb load force F ═ k according to the measured value of the sensor0+k1f1+k2f2+k3f3+…+knfnWherein: n is the number of sensitive units, f1-fnFor each sensitive unit measured value, k0-knAre coefficients fitted by the least squares method.
The multiple linear regression model introduces the pressure sensitive unit measurement values into the model one by one through stepwise regression, and ensures that the regression model only contains significant variables through F test, t test and variable elimination, and the specific establishment steps comprise:
1) establishing a pressure sensitive unit measurement value and a lower limb load reference value data set;
2) introducing the pressure sensitive unit measurement values into the model one by one, and evaluating that when the previously introduced variables become no longer significant due to the introduction of the current variables, the previously introduced variables are deleted to ensure that the regression model only contains significant variables;
3) and (5) circulating the step 2 for a plurality of times until new significance variables can not be introduced, and completing model establishment.
Built-in data processing unit of intelligence bracelet and image display element, wherein: the data processing unit calculates the lower limb load through the lower limb load estimation model and respectively controls the vibration module and the image display unit to send out a rehabilitation training prompt.
The orthopedic lower limb postoperative intelligent rehabilitation application program also provides functions of training plan setting, training data recording, training report checking and rehabilitation prescription downloading for the patient.
The vibration feedback module sends out vibration prompt when the load of the lower limbs of the human body exceeds a reasonable load range, so that the patient can adjust the load of the lower limbs in time.
Technical effects
The invention integrally solves the problem that the lower limb load is lack of effective monitoring means, monitors the lower limb load in real time under the condition of taking measurement accuracy and wearing comfort into consideration, provides lower limb load training prompts for patients, and provides training data checking, uploading and interacting functions for the patients and doctors through application programs.
Compared with the prior art, the invention is specially designed for the postoperative patients of lower limbs, has good wearability and avoids secondary damage to postoperative parts; the lower limb load is estimated through the multiple linear regression model, and the method has the characteristics of simple modeling, high calculation efficiency, good real-time performance and the like; according to the invention, functions of training guidance, data viewing and uploading, training report generation, doctor-patient interaction and the like are provided for the patient through the orthopedic lower limb postoperative intelligent rehabilitation application program, so that the rehabilitation training effect is improved, and the loss of clinical data is avoided.
Drawings
FIG. 1 is a flow chart of an embodiment;
FIG. 2 is a graph comparing a reference value of a force platform with an estimated value of a multiple linear regression model;
FIG. 3 is a schematic structural diagram of the wearable intelligent auxiliary device for load monitoring and rehabilitation training according to the present invention;
FIG. 4 is a schematic view of an installation state of the wearable intelligent auxiliary device for load monitoring and rehabilitation training according to the present invention;
fig. 5 is an information architecture diagram of an intelligent bracelet orthopedic lower limb postoperative rehabilitation application according to the present invention;
in the figure: 1 fabric knee-pad, 2 data acquisition module, 3 bandages, 4 vibrations feedback module, 5 batteries, 6 flexible fixed bands, 7 insoles, 8 film formula pressure sensor, 9 plastic cover, 10 intelligent bracelet, 11 intelligent bracelet, 12 help capable ware, 13 wearing formula heavy burden monitoring and rehabilitation training intelligence auxiliary device.
Detailed Description
As shown in fig. 1, the present embodiment includes: flexible sole pressure measurement shoe-pad, data acquisition module, vibrations feedback module, intelligent bracelet, low limbs heavy burden estimate model and power module, wherein: the flexible sole pressure measuring insole comprises a film type pressure sensor, a plastic sleeve, an insole and a flexible fixing belt; the data acquisition and vibration feedback module transmits data to the intelligent bracelet through the Bluetooth module, the intelligent bracelet receives the data and estimates the total load of the lower limbs according to the measurement value of the sensor through the lower limb load estimation model; the intelligent bracelet controls the vibration feedback module to send out a training prompt, records training data and generates a training report.
The lower limb load estimation model related to the embodiment is a multiple linear regression model, is established based on a large amount of sample data, and ensures that the model only contains significant variables through stepwise regression. Fig. 2 shows a comparison curve of the model estimated value and the force-measuring table reference value, the root mean square error of the model estimation is 50.36N (about 9.90% of the maximum measured value), the correlation coefficient is 0.92, and the measurement requirement of the lower limb load in the walking process is met.
When this embodiment uses, lower limbs postoperative patient can arrange recovered shoes in or functional shoe-pad below with flexible plantar pressure measurement shoe- pad 6, 7, 8, 9 to at shank department fixed knee-pad 1 and bandage 3, data acquisition module 2, 5 and vibrations feedback module 4, before the training, utilize intelligent bracelet 11 to set up the plan of bearing a burden. The patient can carry out the rehabilitation training under the assistance of helping hand ware 12, and the training in-process, data acquisition module 2 sends the plantar pressure data of gathering for intelligent bracelet 11, and intelligent bracelet 11 utilizes low limbs to estimate the model and estimates low limbs heavy burden, and the measuring result presents in intelligent bracelet 11's screen with data and curved form, makes the patient can accurate, know low limbs heavy burden in time. The intelligent bracelet 11 sends vibrations suggestion according to heavy burden dynamics restriction control vibrations module 4, makes the patient in time adjust low limbs heavy burden, keeps low limbs heavy burden at reasonable effectual within range.
The embodiment provides a load training intelligent auxiliary device convenient to wear and a lower limb load estimation method for a lower limb postoperative patient, and is suitable for postoperative lower limb load training, walking function training after diseases and other lower limb rehabilitation training.
The invention uses the flexible sole pressure measuring insole only containing a small number of sensitive units (8-10) to measure the sole pressure, and establishes a multiple linear regression model based on sample data to estimate the lower limb load, and the model has good estimation precision, high calculation efficiency and good real-time property.
The root mean square error estimated by the multivariate linear regression model is 50.36N (about 9.90 percent of the maximum measurement value), the correlation coefficient is 0.92, and the lower limb load measurement requirement in the walking process is met.
Compared with the prior art, the device has the advantages that under the condition that the wearing performance is greatly improved, the multiple linear regression model has good lower limb load estimation precision, the modeling is simple, the calculation efficiency is high, and the device is suitable for real-time monitoring of lower limb load. The device has a feedback function of lower limb weight training, and experiments show that the lower limb weight training can obviously improve the compliance capability of a patient to the lower limb weight strength, thereby improving the rehabilitation effect and ensuring the training safety. The device records training data through the intelligent rehabilitation application program after the lower limb operation of the orthopedics department, generates a training report, facilitates communication between doctors and patients, and is favorable for improving the rehabilitation training effect of the patients.
The foregoing embodiments may be modified in many different ways by those skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (4)
1. The utility model provides a wearing formula heavy burden monitoring and rehabilitation training intelligence auxiliary device which characterized in that includes: flexible sole pressure measurement shoe-pad, data acquisition module, vibrations feedback module and built-in low limbs bear a burden the intelligent bracelet of estimating the model, wherein: the data acquisition circuit receives pressure data of each region of the sole measured by a film type pressure sensor in the flexible sole pressure measuring insole and respectively sends the pressure data to the intelligent bracelet and the vibration feedback module in a wireless mode; the intelligent bracelet calculates and displays the data of the load of the lower limbs of the human body in real time according to the lower limb load estimation model and the pressure data; when the human body lower limb load data from the smart bracelet exceeds a reasonable load range, a vibration module connected with the flexible plantar pressure measuring insole sends out a vibration prompt;
the flexible plantar pressure measuring insole comprises: flexible area is decided and shoe-pad, film formula pressure sensor and the plastic cover that from top to bottom sets gradually, wherein: the flexible shaping belt is respectively connected with the plastic sleeve and the vibration module;
the thin film type pressure sensor is a piezoresistive flexible sole pressure sensor, and 8-10 pressure sensitive units respectively measure the stress of a plurality of local areas of the sole;
the data acquisition module and the vibration feedback module are arranged in a flexible binding band connected with the flexible sole pressure measuring insole so as to adjust the binding position and the tightness according to the injury condition of legs and combine with the flexible sole pressure measuring insole;
the lower limb load estimation model is established based on sample data, specifically is a multiple linear regression model, is established by collecting a large amount of sample data through experiments, and estimates the total lower limb load force F ═ k according to the measured value of the sensor0+k1f1+k2f2+k3f3+…+knfnWherein: n is the number of sensitive units, f1-fnFor each sensitive unit measured value, k0-knAre coefficients fitted by the least squares method.
2. The wearable intelligent auxiliary device for load monitoring and rehabilitation training as claimed in claim 1, wherein said pressure sensitive unit comprises: the shell, the strain resistor arranged in the shell and the lead wire connected with the strain resistor.
3. The wearable intelligent auxiliary device for load monitoring and rehabilitation training as claimed in claim 1, wherein the multiple linear regression model introduces the measured values of the pressure sensitive units into the model one by one through stepwise regression, and ensures that the regression model only contains significant variables through F test, t test and variable elimination, and the specific establishment steps include:
1) establishing a pressure sensitive unit measurement value and a lower limb load reference value data set;
2) introducing the pressure sensitive unit measurement values into the model one by one, and evaluating that when the previously introduced variables become no longer significant due to the introduction of the current variables, the previously introduced variables are deleted to ensure that the regression model only contains significant variables;
3) and (5) circulating the step 2 for a plurality of times until new significance variables can not be introduced, and completing model establishment.
4. The wearable intelligent auxiliary device for load monitoring and rehabilitation training as claimed in claim 1, wherein the smart band is internally provided with a data processing unit and an image display unit, wherein: the data processing unit calculates the lower limb load through the lower limb load estimation model and respectively controls the vibration module and the image display unit to send out a rehabilitation training prompt.
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| CN201911362748.5A CN110974232A (en) | 2019-12-26 | 2019-12-26 | Wearable load monitoring and rehabilitation training intelligent auxiliary device |
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| CN201911362748.5A CN110974232A (en) | 2019-12-26 | 2019-12-26 | Wearable load monitoring and rehabilitation training intelligent auxiliary device |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112618284A (en) * | 2020-12-30 | 2021-04-09 | 安徽三联机器人科技有限公司 | Shoes for assisting lower limbs |
| CN114052724A (en) * | 2022-01-13 | 2022-02-18 | 西安交通大学医学院第一附属医院 | Orthopedics traction abnormity detection system based on artificial intelligence |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202113533U (en) * | 2011-05-10 | 2012-01-18 | 孙涛 | Quantitative load bearing rehabilitation training device for lower limb fracture |
| CN102657535A (en) * | 2012-05-23 | 2012-09-12 | 西安交通大学 | Flexible sole force measuring system |
| CN102670207A (en) * | 2012-05-15 | 2012-09-19 | 北京大学 | Gait analysis method based on plantar pressure |
| CN104280177A (en) * | 2014-09-29 | 2015-01-14 | 东华大学 | Plantar pressure testing device and total-fabric insole with plantar pressure testing device |
| CN104473650A (en) * | 2014-12-25 | 2015-04-01 | 中国科学院合肥物质科学研究院 | Movement energy consumption monitoring shoes based on flexible force sensor and monitoring method of movement energy consumption monitoring shoes |
| CN105054927A (en) * | 2015-07-16 | 2015-11-18 | 西安交通大学 | Biological quantitative assessment method for active participation degree in lower limb rehabilitation system |
| CN105266257A (en) * | 2015-11-05 | 2016-01-27 | 安徽埃力智能科技有限公司 | Intelligent shoe based on whole-foot pressure information acquiring |
| CN106108907A (en) * | 2016-06-17 | 2016-11-16 | 合肥工业大学 | A kind of sole pressure distribution detector |
| CN106821384A (en) * | 2017-02-22 | 2017-06-13 | 安宁 | It is a kind of walk appearance training shoe-pad |
| WO2017179948A2 (en) * | 2016-04-15 | 2017-10-19 | 김미정 | Walking assistance device |
| US20170315037A9 (en) * | 2013-02-13 | 2017-11-02 | Board Of Regents, The University Of Texas System | Sensor Assembly, Method, and Device for Monitoring Shear Force and Pressure on a Structure |
| CN107307869A (en) * | 2017-06-13 | 2017-11-03 | 哈尔滨医科大学 | Intelligent lower limb heavy burden safeguard protection, training effect monitoring and gait analysis device |
| US20170319368A1 (en) * | 2016-05-04 | 2017-11-09 | Allen Joseph Selner | Instrumented orthotic |
| US10045439B2 (en) * | 2012-09-11 | 2018-08-07 | L.I.F.E. Corporation S.A. | Garments having stretchable and conductive ink |
| US20190090812A1 (en) * | 2017-09-25 | 2019-03-28 | Bragi GmbH | Smart socks |
| CN110025316A (en) * | 2019-04-08 | 2019-07-19 | 广东工业大学 | A kind of low-power consumption can radio upgrade intelligent shoe embedded system |
-
2019
- 2019-12-26 CN CN201911362748.5A patent/CN110974232A/en active Pending
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202113533U (en) * | 2011-05-10 | 2012-01-18 | 孙涛 | Quantitative load bearing rehabilitation training device for lower limb fracture |
| CN102670207A (en) * | 2012-05-15 | 2012-09-19 | 北京大学 | Gait analysis method based on plantar pressure |
| CN102657535A (en) * | 2012-05-23 | 2012-09-12 | 西安交通大学 | Flexible sole force measuring system |
| US10045439B2 (en) * | 2012-09-11 | 2018-08-07 | L.I.F.E. Corporation S.A. | Garments having stretchable and conductive ink |
| US20170315037A9 (en) * | 2013-02-13 | 2017-11-02 | Board Of Regents, The University Of Texas System | Sensor Assembly, Method, and Device for Monitoring Shear Force and Pressure on a Structure |
| CN104280177A (en) * | 2014-09-29 | 2015-01-14 | 东华大学 | Plantar pressure testing device and total-fabric insole with plantar pressure testing device |
| CN104473650A (en) * | 2014-12-25 | 2015-04-01 | 中国科学院合肥物质科学研究院 | Movement energy consumption monitoring shoes based on flexible force sensor and monitoring method of movement energy consumption monitoring shoes |
| CN105054927A (en) * | 2015-07-16 | 2015-11-18 | 西安交通大学 | Biological quantitative assessment method for active participation degree in lower limb rehabilitation system |
| CN105266257A (en) * | 2015-11-05 | 2016-01-27 | 安徽埃力智能科技有限公司 | Intelligent shoe based on whole-foot pressure information acquiring |
| WO2017179948A2 (en) * | 2016-04-15 | 2017-10-19 | 김미정 | Walking assistance device |
| US20170319368A1 (en) * | 2016-05-04 | 2017-11-09 | Allen Joseph Selner | Instrumented orthotic |
| CN106108907A (en) * | 2016-06-17 | 2016-11-16 | 合肥工业大学 | A kind of sole pressure distribution detector |
| CN106821384A (en) * | 2017-02-22 | 2017-06-13 | 安宁 | It is a kind of walk appearance training shoe-pad |
| CN107307869A (en) * | 2017-06-13 | 2017-11-03 | 哈尔滨医科大学 | Intelligent lower limb heavy burden safeguard protection, training effect monitoring and gait analysis device |
| US20190090812A1 (en) * | 2017-09-25 | 2019-03-28 | Bragi GmbH | Smart socks |
| CN110025316A (en) * | 2019-04-08 | 2019-07-19 | 广东工业大学 | A kind of low-power consumption can radio upgrade intelligent shoe embedded system |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112618284A (en) * | 2020-12-30 | 2021-04-09 | 安徽三联机器人科技有限公司 | Shoes for assisting lower limbs |
| CN112618284B (en) * | 2020-12-30 | 2023-10-24 | 安徽三联机器人科技有限公司 | Footwear for assisting lower limbs |
| CN114052724A (en) * | 2022-01-13 | 2022-02-18 | 西安交通大学医学院第一附属医院 | Orthopedics traction abnormity detection system based on artificial intelligence |
| CN114052724B (en) * | 2022-01-13 | 2022-09-09 | 西安交通大学医学院第一附属医院 | Orthopedics traction abnormity detection system based on artificial intelligence |
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