CN113367438A - Rehabilitation shoe based on multi-sensor fusion, monitoring and analyzing method and storage medium - Google Patents
Rehabilitation shoe based on multi-sensor fusion, monitoring and analyzing method and storage medium Download PDFInfo
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- CN113367438A CN113367438A CN202110577284.0A CN202110577284A CN113367438A CN 113367438 A CN113367438 A CN 113367438A CN 202110577284 A CN202110577284 A CN 202110577284A CN 113367438 A CN113367438 A CN 113367438A
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- A—HUMAN NECESSITIES
- A43—FOOTWEAR
- A43B—CHARACTERISTIC FEATURES OF FOOTWEAR; PARTS OF FOOTWEAR
- A43B13/00—Soles; Sole-and-heel integral units
- A43B13/14—Soles; Sole-and-heel integral units characterised by the constructive form
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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
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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
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1116—Determining posture transitions
- A61B5/1117—Fall detection
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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
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/112—Gait analysis
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Abstract
The application discloses recovered shoes based on multisensor fuses, including the sole, be equipped with data acquisition module in the sole, data acquisition module include the processing unit, with pressure detection unit, six sensor unit and the wireless communication unit that the processing unit electricity is connected, pressure detection unit includes a plurality of pressure sensor that distribute in the sole and be used for acquireing the plantar pressure value, six sensor unit are including the triaxial acceleration sensor that is used for acquireing triaxial acceleration value and the triaxial angular velocity sensor that is used for acquireing triaxial angular velocity value. The application provides a recovered shoes based on pressure detection unit and six sensor units to through the unusual data of discernment user's plantar pressure and moving acceleration to whether accurate discovery user is in the unusual gait of tumbleing, effectively in time discover or rescue the user who is in the unusual gait of tumbleing.
Description
[ technical field ] A method for producing a semiconductor device
The application relates to the technical field of wearable electronic equipment, in particular to a pair of rehabilitation shoes based on multi-sensor fusion, a monitoring and analyzing method and a storage medium.
[ background of the invention ]
Human gait has important referential significance in reflecting the health condition of the body, the motor function of the lower limbs and diseases.
The elderly are susceptible to dyskinesia due to aging of nervous system and damage to motor system. When the nervous system or the motor system is affected by diseases, the motor function of the human body is also impaired, and the external gait is affected, which is manifested as abnormal gait. Meanwhile, the elderly are easy to fall down and have other dangerous situations due to weakened physical functions and inconvenient actions, so that other parts such as body tissues, organs and the like are damaged, and even the life of the elderly is threatened seriously. Therefore, the gait monitoring and analyzing device has great significance for monitoring and analyzing the gait of the human body in real time, finding abnormal activities and body changes in time, providing early warning and corresponding rescue mechanisms, or assisting rehabilitation training and the like. In order to effectively identify whether a user has a falling state or an abnormal gait, a rehabilitation shoe for monitoring the gait of the sole of the user based on multiple dimensions and a monitoring and analyzing method for fusing multiple dimensions of data acquired by the rehabilitation shoe need to be designed.
[ summary of the invention ]
In order to accurately monitor abnormal gait of falling or pre-falling, the application provides a rehabilitation shoe based on multi-sensor fusion, a monitoring and analyzing method and a storage medium.
The application is realized by the following technical scheme:
recovered shoes based on multisensor fuses, including the sole, be equipped with data acquisition module in the sole, data acquisition module include processing unit, with pressure detection unit, six sensor unit and the wireless communication unit that the processing unit electricity is connected, pressure detection unit includes a plurality of distribution and is used for acquireing the pressure sensor of sole pressure value in the sole, six sensor unit are including the triaxial acceleration sensor that is used for acquireing the triaxial acceleration value and the triaxial angular velocity sensor that is used for acquireing the triaxial angular velocity value.
The rehabilitation shoe based on multi-sensor fusion comprises a sole, a middle layer and a shoe pad layer, wherein the middle layer is positioned above the sole, the shoe pad layer is positioned above the middle layer, and the pressure detection unit is arranged on the surface of the middle layer and is embedded into the shoe pad layer.
According to the rehabilitation shoe based on multi-sensor fusion, the number of the pressure sensors at the two ends of the middle layer is greater than that of the pressure sensors in the middle of the middle layer.
According to the rehabilitation shoe based on multi-sensor fusion, the data acquisition module further comprises a temperature and humidity sensor unit electrically connected with the processing unit, and the temperature and humidity sensor unit comprises temperature and humidity sensors arranged at two ends of the middle layer.
The rehabilitation shoe based on multi-sensor fusion is characterized in that the data acquisition module further comprises a GPS positioning module, a voice prompt unit and a power management unit which are electrically connected with the processing unit, the six-axis sensor unit, the wireless communication unit, the GPS positioning module, the voice prompt unit and the power management unit are integrated in a circuit board, a groove for accommodating the circuit board is formed in the bottom layer, and a battery electrically connected with the power management unit is arranged in the groove.
According to the rehabilitation shoe based on multi-sensor fusion, the data acquisition module further comprises a charging management unit electrically connected with the battery, and a power interface electrically connected with the charging management unit and a charging indicator light electrically connected with the processing unit are arranged on the outer side of the sole.
The application also provides a monitoring and analyzing method, which comprises the following steps:
s1, receiving plantar pressure value data which are sent every other preset time period by the wireless communication unit and are acquired by the pressure detection unit in the corresponding preset time period, and three-axis acceleration value data and three-axis angular velocity value data which are acquired by the six-axis sensor unit in the corresponding preset time period;
s2, taking two adjacent preset time periods as gait analysis windows, and taking the maximum plantar pressure value data at each moment in the gait analysis windows as gait analysis window data;
s3, judging whether abnormal movement gait periods exist in the gait analysis window or not according to the maximum plantar pressure value data change in the gait analysis window;
s4, if an abnormal movement gait period exists, in a gait analysis window, taking the moment which is closest to the abnormal movement gait period before the abnormal movement gait period and the maximum plantar pressure value of which is equal to a preset gait threshold value as an initial time point, taking the time period after the initial time point as an abnormal movement gait analysis window, taking the maximum plantar pressure value, the three-axis acceleration value and the three-axis angular velocity value of each moment in the abnormal movement gait analysis window as abnormal movement gait analysis window data, and synchronously performing the following steps:
s51, judging whether the maximum plantar pressure value at each moment in the abnormal movement gait analysis window is larger than a preset gait threshold value or not, and if not, outputting a suspected falling result;
s52, calculating a triaxial acceleration average value in the abnormal movement gait analysis window according to triaxial acceleration values at each moment in the abnormal movement gait analysis window, calculating a correlation coefficient between the triaxial acceleration average value and static triaxial acceleration (0,0, g), judging whether the calculated correlation coefficient is larger than a preset correlation coefficient threshold value or not, and outputting a suspected falling result if the correlation coefficient is not larger than the preset correlation coefficient threshold value, wherein g is gravity acceleration;
s53, calculating a corresponding combined acceleration value according to the triaxial acceleration value at each moment in the abnormal movement gait analysis window, calculating a combined acceleration change value variance according to the change of the combined acceleration value in the abnormal movement gait analysis window, judging whether the calculated combined acceleration change value variance is larger than a preset variance threshold value, and if so, outputting a suspected falling result;
s6, if any two of the steps S51, S52, and S53 output the result of the suspected fall, the fall status result is output.
The monitoring and analyzing method described above specifically includes, in step S3, the following steps:
and identifying whether the maximum plantar pressure value data change of the corresponding time period in the gait analysis window meets the switching between the support phase and the swing phase, if so, judging the gait time period to be the normal movement gait time period, and if not, judging the gait time period to be the abnormal movement gait time period.
The present application further provides a storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the monitoring and analyzing method described above.
Compared with the prior art, the method has the following advantages:
1. the application provides a recovered shoes based on pressure detection unit and six sensor units, through the unusual data of discernment user's plantar pressure and acceleration of movement to through the integration of these data, but multi-angle, multidimension degree reaction gait characteristic, whether be in the unusual gait of tumbleing with accurate discovery user, effectively in time discover or rescue the user who is in the unusual gait of tumbleing.
2. The application provides a monitoring and analyzing method, whether an abnormal movement gait period exists in a gait analysis window is identified, and whether a user falls down or not is accurately judged according to multi-dimensional data such as maximum plantar pressure value data, three-axis acceleration values and combined acceleration values when the abnormal movement gait period exists.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below.
Fig. 1 is a structural schematic diagram of a rehabilitation shoe based on multi-sensor fusion.
Fig. 2 is a block diagram of the structure of the data acquisition module.
[ detailed description ] embodiments
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments that can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present application belong to the protection scope of the present application.
As shown in fig. 1 and fig. 2, the embodiment discloses a rehabilitation shoe based on multi-sensor fusion, including sole 1 and upper of a shoe 4, be equipped with data acquisition module 2 in the sole 1, data acquisition module 2 includes processing unit 21, with pressure detection unit 22, six sensor unit 23 and the wireless communication unit 24 that processing unit 21 electricity is connected, specifically, processing unit 21 is ARM processor, singlechip or DSP, wireless communication unit 24 can be 4G communication module or WIFI. Pressure detecting element 22 includes a plurality of pressure sensor who distributes in sole 1 and be used for acquireing the sole pressure value, and the pressure sensor of this embodiment can adopt current pressure sensor, six sensor unit 23 is including the triaxial acceleration sensor who is used for acquireing the triaxial acceleration value and the triaxial angular velocity sensor who is used for acquireing triaxial angular velocity value, and the triaxial acceleration sensor and the triaxial angular velocity sensor of this embodiment can adopt current triaxial acceleration sensor and triaxial angular velocity sensor, and wherein, in triaxial acceleration value and the triaxial angular velocity value, set for the direction that the X axle is forward for the people to advance, and the right side that the directional human body of Y axle forward is on a parallel with both shoulders, the perpendicular X axle of Z axle, Y axle and downwards. The processing unit 21 receives the plantar pressure value, the triaxial acceleration value and the triaxial angular velocity value, performs operations such as filtering, amplification, analog-to-digital conversion and the like on the data, forms a data packet according to a certain rule, transmits the data to the background server through the wireless communication unit 24 for monitoring and analysis, receives and analyzes the data by the background server, and returns an analysis recognition result to the processing unit 21 after passing through a corresponding algorithm. In the embodiment, the rehabilitation shoes based on the pressure detection unit and the six-axis sensor unit are provided, whether the user is in abnormal gait due to falling is accurately found by identifying abnormal data of sole pressure and moving acceleration of the user, and the user in the abnormal gait due to falling is effectively and timely found or rescued. The gait characteristics can be reflected in multiple angles and multiple dimensions by data fusion acquired by the multiple sensors, and accurate identification and analysis results can be obtained.
Further, in order to conveniently arrange a pressure detection unit and enable the pressure detection unit to effectively obtain the plantar pressure of a user, the sole 1 includes a bottom layer 11, a middle layer 12 located above the bottom layer 11 and an insole layer 13 located above the middle layer 12, the middle layer 12 is an FPC, and the pressure detection unit 22 is arranged on the surface of the middle layer 12 and embedded into the insole layer 13.
Further, a plurality of pressure sensors are distributed on the surface of the middle layer 12 according to a certain rule, in order to match the foot shape of a human body, the number of the pressure sensors at the two ends of the middle layer 12 is more than that of the pressure sensors in the middle of the middle layer 12, namely, a large number of pressure sensors are arranged on the toe cap and the tail of the shoe. The specific number may be set according to the accuracy requirements of the pressure data. Further, data acquisition module 2 still include with the humiture sensor unit 25 that the processing unit 21 electricity is connected, humiture sensor unit 25 is including locating the humiture sensor at the both ends of middle level 12, on humiture sensor also located tip and the tail of a shoe promptly, according to temperature, humidity around in the shoes that humiture sensor unit 25 gathered, judge whether the environment in the shoes is normal, guarantee to be difficult for breeding the bacterium in the shoes. Corresponding hidden circuits are arranged among the middle layer 12, the pressure sensors and the two temperature and humidity sensors, so that the pressure detection unit 22 and the temperature and humidity sensor unit 25 are connected with circuit elements in the processing unit 21.
Further, the data acquisition module 2 further includes a GPS positioning module 26, a voice prompt unit 27 and a power management unit 28 electrically connected to the processing unit 21, and the GPS positioning module 26 is used to realize a track tracking function to prevent the old from losing. For convenience of installation, the processing unit 21, the six-axis sensor unit 23, the wireless communication unit 24, the GPS positioning module 26, the voice prompt unit 27 and the power management unit 28 are integrated in a circuit board 29, the bottom layer 11 is provided with a groove 14 for accommodating the circuit board 29, and a battery 30 electrically connected with the power management unit 28 is arranged in the groove 14. Specifically, the voice prompt unit 27 may be a buzzer. The power management unit 28 includes conventional power circuits such as a filter circuit and a regulator circuit to convert the output voltage of the battery 30 into voltages required by the circuit modules connected thereto, respectively. The battery 30 may be a lithium battery.
Further, data acquisition module 2 still include with the management unit 31 that charges that battery 30 electricity is connected, 1 outside of sole be equipped with the management unit 31 electricity that charges be connected power source 32 and with the charge indicator 33 that the processing unit 21 electricity is connected, power source 32 is for magnetism to inhale the interface that charges, through magnetism inhale interface and commercial power connection that charges to acquire the electric energy of commercial power, can accelerate the charging speed, realize waterproofly, easy operation effect. The charge indicator lamp 33 may be used to indicate the charge status.
The embodiment also provides a monitoring and analyzing method based on the rehabilitation shoes, which comprises the following steps:
s1, receiving the data of the sole pressure value obtained by the pressure detection unit 22 in the corresponding preset time period and the data of the three-axis acceleration value and the three-axis angular velocity value obtained by the six-axis sensor unit 23 in the corresponding preset time period, which are sent by the wireless communication unit 24 every preset time period.
And S2, taking two adjacent preset time periods as gait analysis windows, and taking the maximum plantar pressure value data at each moment in the gait analysis windows as gait analysis window data.
And S3, judging whether abnormal movement gait periods exist in the gait analysis window or not according to the maximum plantar pressure value data change in the gait analysis window.
The method specifically comprises the following steps:
and identifying whether the maximum plantar pressure value data change of the corresponding time period in the gait analysis window meets the switching between the support phase and the swing phase, if so, judging the gait time period to be the normal movement gait time period, and if not, judging the gait time period to be the abnormal movement gait time period. Specifically, a preset gait threshold value is set according to the weight and the height of a human body, and when the maximum plantar pressure value is smaller than the preset gait threshold value, the user is in a swing phase; and when the maximum sole pressure value is larger than the preset gait threshold value, the user is in the support phase. Therefore, in the corresponding time period in the gait analysis window, whether the maximum plantar pressure value data change meets the switching between the support phase and the swing phase or not is identified by judging the quantity relation between the maximum plantar pressure value and the preset gait threshold value.
S4, if an abnormal movement gait period exists, in a gait analysis window, taking the moment which is closest to the abnormal movement gait period before the abnormal movement gait period and the maximum plantar pressure value of which is equal to a preset gait threshold value as an initial time point, taking the time period after the initial time point as an abnormal movement gait analysis window, taking the maximum plantar pressure value, the three-axis acceleration value and the three-axis angular velocity value of each moment in the abnormal movement gait analysis window as abnormal movement gait analysis window data, and synchronously performing the following steps:
and S51, judging whether the maximum plantar pressure value at each moment in the abnormal movement gait analysis window is larger than a preset gait threshold value or not, and if not, outputting a suspected falling result.
S52, calculating a triaxial acceleration average value in the abnormal movement gait analysis window according to triaxial acceleration values at each moment in the abnormal movement gait analysis window, calculating a correlation coefficient between the triaxial acceleration average value and static triaxial acceleration (0,0, g), judging whether the calculated correlation coefficient is larger than a preset correlation coefficient threshold value, and if not, outputting a suspected falling result, wherein g is gravity acceleration.
S53, calculating a corresponding combined acceleration value according to the triaxial acceleration value at each moment in the abnormal movement gait analysis window, calculating a combined acceleration change value variance according to the change of the combined acceleration value in the abnormal movement gait analysis window, judging whether the calculated combined acceleration change value variance is larger than a preset variance threshold value, and if so, outputting a suspected falling result. At the moment when the user falls, at least one direction acceleration changes rapidly and complexly, so that the variance of the total acceleration change value is larger if the user falls.
S6, if any two of the steps S51, S52, and S53 output the result of the suspected fall, the fall status result is output. The voice prompt unit 27 may sound an alarm according to the output fall state result.
The present embodiment also provides a storage medium, wherein the storage medium stores a computer program, and the computer program is executed by a processor to perform the steps of the monitoring and analyzing method.
The rehabilitation shoes of the embodiment can also be used for diagnosis and prevention and rehabilitation training of lower limb movement disorder caused by motor system diseases and nervous system diseases. Abnormal gaits caused by common motor system diseases and nervous system diseases comprise but are not limited to limping, tiptoe, tremor and the like, so the monitoring and diagnosis purposes can be achieved by identifying the abnormal gaits and the normal gaits.
Recovered shoes also can assist recovered crowd to carry out the rehabilitation training, if monitor step length, step frequency, pace reach the rehabilitation training requirement of predetermineeing through the APP, if not reach then carry out the suggestion through voice prompt unit 27. Meanwhile, the historical values of the parameters can be recorded, and the severity of abnormal gait caused by a disease state can be identified through the change trend of the parameters, so that rehabilitation effect evaluation of rehabilitation people can be assisted.
The foregoing is illustrative of the various embodiments provided in connection with the detailed description and the specific implementations of the application are not intended to be limited to the illustrations. Similar or identical methods, structures, etc. as used herein, or several technical deductions or substitutions made on the premise of the idea of the present application, should be considered as the protection scope of the present application.
Claims (9)
1. Recovered shoes based on multisensor fuses, including sole (1), its characterized in that, be equipped with data acquisition module (2) in sole (1), data acquisition module (2) including processing unit (21), with pressure detection unit (22), six sensor unit (23) and wireless communication unit (24) that processing unit (21) electricity is connected, pressure detection unit (22) include a plurality of distribute in sole (1) and be used for acquireing the pressure sensor of sole pressure value, six sensor unit (23) are including the triaxial acceleration sensor that is used for acquireing the triaxial acceleration value and the triaxial angular velocity sensor that is used for acquireing triaxial angular velocity value.
2. The rehabilitation shoe based on multi-sensor fusion of claim 1, characterized in that the sole (1) comprises a bottom layer (11), a middle layer (12) above the bottom layer (11) and an insole layer (13) above the middle layer (12), and the pressure detection unit (22) is arranged on the surface of the middle layer (12) and embedded in the insole layer (13).
3. The rehabilitation shoe based on multi-sensor fusion of claim 2, characterized in that the number of pressure sensors at both ends of the middle layer (12) is greater than the number of pressure sensors in the middle of the middle layer (12).
4. The rehabilitation shoe based on multi-sensor fusion of claim 2, wherein the data acquisition module (2) further comprises a temperature and humidity sensor unit (25) electrically connected with the processing unit (21), and the temperature and humidity sensor unit (25) comprises temperature and humidity sensors arranged at two ends of the middle layer (12).
5. The rehabilitation shoe based on multi-sensor fusion of claim 2, wherein the data acquisition module (2) further comprises a GPS positioning module (26), a voice prompt unit (27) and a power management unit (28) which are electrically connected with the processing unit (21), the six-axis sensor unit (23), the wireless communication unit (24), the GPS positioning module (26), the voice prompt unit (27) and the power management unit (28) are integrated in a circuit board (29), the bottom layer (11) is provided with a groove (14) for accommodating the circuit board (29), and a battery (30) which is electrically connected with the power management unit (28) is arranged in the groove (14).
6. The rehabilitation shoe based on multi-sensor fusion of claim 5, characterized in that the data acquisition module (2) further comprises a charging management unit (31) electrically connected with the battery (30), and a power interface (32) electrically connected with the charging management unit (31) and a charging indicator light (33) electrically connected with the processing unit (21) are arranged on the outer side of the sole (1).
7. The monitoring and analyzing method is characterized by comprising the following steps:
s1, receiving the sole pressure value data which are sent by the wireless communication unit (24) every other preset time period and are acquired by the pressure detection unit (22) in the corresponding preset time period, and the triaxial acceleration value data and the triaxial angular velocity value data which are acquired by the six-axis sensor unit (23) in the corresponding preset time period;
s2, taking two adjacent preset time periods as gait analysis windows, and taking the maximum plantar pressure value data at each moment in the gait analysis windows as gait analysis window data;
s3, judging whether abnormal movement gait periods exist in the gait analysis window or not according to the maximum plantar pressure value data change in the gait analysis window;
s4, if an abnormal movement gait period exists, in a gait analysis window, taking the moment which is closest to the abnormal movement gait period before the abnormal movement gait period and the maximum plantar pressure value of which is equal to a preset gait threshold value as an initial time point, taking the time period after the initial time point as an abnormal movement gait analysis window, taking the maximum plantar pressure value, the three-axis acceleration value and the three-axis angular velocity value of each moment in the abnormal movement gait analysis window as abnormal movement gait analysis window data, and synchronously performing the following steps:
s51, judging whether the maximum plantar pressure value at each moment in the abnormal movement gait analysis window is larger than a preset gait threshold value or not, and if not, outputting a suspected falling result;
s52, calculating a triaxial acceleration average value in the abnormal movement gait analysis window according to triaxial acceleration values at each moment in the abnormal movement gait analysis window, calculating a correlation coefficient between the triaxial acceleration average value and static triaxial acceleration (0,0, g), judging whether the calculated correlation coefficient is larger than a preset correlation coefficient threshold value or not, and outputting a suspected falling result if the correlation coefficient is not larger than the preset correlation coefficient threshold value, wherein g is gravity acceleration;
s53, calculating a corresponding combined acceleration value according to the triaxial acceleration value at each moment in the abnormal movement gait analysis window, calculating a combined acceleration change value variance according to the change of the combined acceleration value in the abnormal movement gait analysis window, judging whether the calculated combined acceleration change value variance is larger than a preset variance threshold value, and if so, outputting a suspected falling result;
s6, if any two of the steps S51, S52, and S53 output the result of the suspected fall, the fall status result is output.
8. The monitoring and analyzing method of claim 7, wherein in step S3, the method specifically includes the following steps:
and identifying whether the maximum plantar pressure value data change of the corresponding time period in the gait analysis window meets the switching between the support phase and the swing phase, if so, judging the gait time period to be the normal movement gait time period, and if not, judging the gait time period to be the abnormal movement gait time period.
9. Storage medium having a computer program stored thereon, characterized in that the computer program, when being executed by a processor, is adapted to carry out the steps of the method of claim 7 or 8.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115486837A (en) * | 2022-09-22 | 2022-12-20 | 北京戴来科技有限公司 | Gait analysis method and system and device for improving walking disorder |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115486837A (en) * | 2022-09-22 | 2022-12-20 | 北京戴来科技有限公司 | Gait analysis method and system and device for improving walking disorder |
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