A gait analysis processing method and system based on time-space parameters, insole and shoe

Abstract

This invention discloses a gait analysis processing method, system, insole, and shoe based on time-space parameters. The method includes the following steps: acquiring gait data within multiple adjacent walking cycles; preprocessing the gait data to obtain gait classification data; processing the gait classification data to obtain gait speed data, gait frequency data, and gait cycle data; and obtaining gait analysis data based on the gait speed data, gait frequency data, and gait cycle data. The system includes a sensor group and a data processor. The sensor group performs real-time detection and transmits the detected gait data to the data processor. The data processor processes the gait data to obtain gait speed data, gait frequency data, and gait cycle data, and also obtains gait analysis data based on the gait speed data, gait frequency data, and gait cycle data. The system acquires gait data in real time and accurately converts it into high-level gait analysis results.

Description

Technical Field

[0001] This invention relates to the field of gait analysis and processing, and specifically to a gait analysis and processing method, system, insole, and shoe based on time and space parameters. Background Technology

[0002] Gait typically refers to the posture and behavioral characteristics of the lower limbs of a target object (e.g., a pedestrian) during walking, reflecting the target object's walking function level. Gait analysis is a key technology for understanding human movement and state. Currently, gait analysis technology has been widely applied in many important fields such as clinical diagnosis, sports science, human-computer interaction, and the Internet of Things (IoT).

[0003] Existing gait analysis solutions are almost entirely limited to indoor environments, making them unsuitable for outdoor or complex and variable environments. Furthermore, the analysis techniques themselves suffer from limitations such as single monitoring dimensions, failing to acquire comprehensive dynamic data. Consequently, the understanding and analysis of gait are not deep or accurate enough. Summary of the Invention

[0004] The technical problem this invention aims to solve is that existing gait analysis solutions are almost entirely limited to indoor environments, making them unsuitable for outdoor or complex and variable environments. Furthermore, the analysis techniques themselves suffer from limitations, such as single monitoring dimensions and the inability to acquire comprehensive dynamic data. This results in insufficient depth and accuracy in the final understanding and analysis of gait. This invention provides a gait analysis processing method based on time-space parameters, a gait analysis processing system, an insole, and a shoe. The method acquires gait data within multiple adjacent walking cycles; preprocesses the gait data to obtain gait classification data; processes the gait classification data to obtain gait speed data, gait frequency data, and gait cycle data, and then obtains gait analysis data. This allows for real-time acquisition of gait data and accurate conversion into high-level gait analysis results, enabling real-time and accurate analysis and monitoring of complex gait patterns in users, thus overcoming the shortcomings of existing technologies.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solutions: Firstly, a gait analysis processing method based on time-space parameters includes the following steps: Acquire gait data over multiple adjacent walking cycles; The gait data is preprocessed to obtain gait classification data; Based on the gait classification data, gait speed data, gait frequency data, and gait cycle data are obtained. Gait analysis data is obtained based on the gait speed data, the gait frequency data, and the gait cycle data.

[0006] The above-mentioned gait analysis processing method based on time-space parameters includes the following method for obtaining the gait speed data from the gait classification data: The ground contact time of the left and right feet in the gait data is recorded as t. Li With t Ri ; Calculate the step speed S for each adjacent step. i =L i / (t) Ri -t Li-1 ) or S i =L i / (t) Li+1 -t Ri ); The average step speed is calculated as S = ΣS i / Σ i .

[0007] The above-mentioned gait analysis processing method based on time-space parameters includes the following method for obtaining the gait frequency data from the gait classification data: The time of foot contact with the ground in the gait data is denoted as T. i ; The time it takes for adjacent left and right feet to alternate during walking is denoted as Δt. i =T i+1 -T i ; The step frequency during alternating steps of adjacent left and right feet during walking is denoted as f. i =1 / Δt i ; The average step frequency is calculated as F = Σf i / Σ i .

[0008] The above-mentioned gait analysis processing method based on time-space parameters includes the following method for obtaining the gait cycle data from the gait classification data: The time it takes for the left heel and right heel to land sequentially in the gait data is recorded as t. Li t Li+1 t Li+2 ... t Li+n With t Ri t Ri+1 t Ri+2 ... t Ri+n ; Calculate the gait period of the left foot in the i-th step as P. Li =t Li+1 -t Li ; Calculate the gait period of the right foot in the i-th step as P.Ri =t Ri+1 -t Ri ; Calculate the average gait period of the left foot as P. L =ΣP Li / Σ i ; Calculate the average gait period of the right foot as P. R =ΣP Ri / Σ i ; The average gait period of the left and right feet is calculated as P = (P L +P R ) / 2; The gait cycle data includes the gait cycle of the i-th step of the left foot, the gait cycle of the i-th step of the right foot, the average gait cycle of the left foot, the average gait cycle of the right foot, and the average gait cycle of both feet.

[0009] Secondly, a gait analysis and processing system based on time-space parameters includes a sensor group and a data processor, wherein the sensor group performs real-time detection and transmits the detected gait data to the data processor; The data processor is used to process the gait data to obtain gait speed data, gait frequency data, and gait cycle data, and is also used to obtain gait analysis data based on the gait speed data, the gait frequency data, and the gait cycle data.

[0010] A gait analysis and processing system based on time-space parameters, wherein the sensor group includes a triaxial accelerometer, a triaxial angular velocity sensor, a triaxial magnetometer, and a plantar pressure sensor. The triaxial accelerometer acquires data in the x, y, and z directions; the triaxial angular velocity sensor acquires relevant data in the α, β, and γ directions; the triaxial magnetometer acquires data related to east-west, north-south, and sky-ground directions; and the plantar pressure sensor covers the entire sole of the foot and acquires plantar pressure-related data. The sampling frequency range is 20-100 Hz (10-50 ms). The data processor has a built-in filtering module and a coordinate system transformation module; The filtering module is used to filter and clean the gait data. The posture of the foot in space changes during walking. The data collected by each sensor in real time is filtered and cleaned and then converted into a horizontal coordinate system. The coordinate system transformation module is used to transform the gait data after filtering and cleaning into coordinate system data.

[0011] Thirdly, an insole comprising a gait analysis and processing system based on time-space parameters as described in the second aspect.

[0012] Fourthly, a shoe comprising a gait analysis and processing system based on time-space parameters as described in the second aspect.

[0013] Fifthly, a chip comprising: a processor for retrieving and running a computer program from a memory, causing a device having the chip mounted to perform: the method described in the first aspect.

[0014] The technical solutions provided by the gait analysis and processing method, system, insole, and shoe of the present invention, as described above, have the following technical effects: It acquires gait data within multiple adjacent walking cycles; preprocesses the gait data to obtain gait classification data; processes the gait classification data to obtain gait speed data, gait frequency data, and gait cycle data, and obtains gait analysis data. It can acquire gait data in real time, accurately convert it into high-level gait analysis results, and obtain multi-dimensional prediction data for both feet, so as to realize real-time and accurate analysis and monitoring of the user's complex gait patterns. Attached Figure Description

[0015] Figure 1 This is a flowchart of a gait analysis and processing method based on time-space parameters according to the present invention; Figure 2 This is a schematic diagram of the gait analysis and processing system based on time and space parameters according to the present invention.

[0016] The accompanying figure is labeled as follows: Sensor group 100, data processor 200, filtering module 201, coordinate system transformation module 202. Detailed Implementation

[0017] In order to make the technical means, inventive features, objectives and effects of the invention easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to specific illustrations. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0018] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be noted that the structures, proportions, sizes, etc., illustrated in the accompanying drawings of this specification are only used to complement the content disclosed in the specification for those skilled in the art to understand and read, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0020] Furthermore, the terms such as "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity of description and are not intended to limit the scope of the invention. Any changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

[0021] This invention provides a gait analysis processing method, system, insole, and shoe, with the aim of acquiring gait data within multiple adjacent walking cycles; preprocessing the gait data to obtain gait classification data; processing the gait classification data to obtain gait speed data, gait frequency data, and gait cycle data, and obtaining gait analysis data. It can acquire gait data in real time, accurately convert it into high-level gait analysis results, and obtain multi-dimensional prediction data of both feet, so as to realize real-time and accurate analysis and monitoring of the user's complex gait patterns.

[0022] like Figure 1 As shown, in the first embodiment, a first aspect, a gait analysis processing method based on time-space parameters includes the following steps: Acquire gait data over multiple adjacent walking cycles; Gait data is preprocessed to obtain gait classification data; Gait classification data is processed to obtain gait speed data, gait frequency data, and gait cycle data. Gait analysis data is obtained based on gait speed data, gait frequency data, and gait cycle data.

[0023] The above-mentioned gait analysis processing method based on time-space parameters includes the following method for obtaining gait speed data from gait classification data: The ground contact time of the left and right feet in the gait data is denoted as t. Li With t Ri ; Calculate the step speed S for each adjacent step. i =L i / (t) Ri -t Li-1 ) or S i =L i / (t) Li+1 -t Ri ); The average step speed is calculated as S = ΣS i / Σ i .

[0024] The above-mentioned gait analysis processing method based on time-space parameters includes the following method for obtaining gait frequency data from gait classification data: The time of foot contact with the ground in the gait data is denoted as T. i ; The time it takes for adjacent left and right feet to alternate during walking is denoted as Δt. i =T i+1 -T i ; The step frequency during alternating steps of adjacent left and right feet during walking is denoted as f. i =1 / Δt i ; The average step frequency is calculated as F = Σf i / Σ i .

[0025] The above-mentioned gait analysis processing method based on time-space parameters includes the following method for obtaining gait cycle data from gait classification data: The time it takes for the left heel and right heel to strike the ground sequentially in the gait data is denoted as t. Li t Li+1 t Li+2 ... t Li+n With t Ri t Ri+1 t Ri+2 ... t Ri+n ; Calculate the gait period of the left foot in the i-th step as P. Li =t Li+1 -t Li ; Calculate the gait period of the right foot in the i-th step as P. Ri =t Ri+1 -t Ri ; Calculate the average gait period of the left foot as P. L =ΣP Li / Σ i ; Calculate the average gait period of the right foot as P. R =ΣP Ri / Σ i ; The average gait period of the left and right feet is calculated as P = (P L +P R ) / 2; Gait cycle data includes the gait cycle of the i-th step of the left foot, the gait cycle of the i-th step of the right foot, the average gait cycle of the left foot, the average gait cycle of the right foot, and the average gait cycle of both feet.

[0026] like Figure 2 As shown, in the second embodiment, a second aspect, a gait analysis and processing system includes a sensor group 100 and a data processor 200. The sensor group 100 performs real-time detection and transmits the detected gait data to the data processor 200. The data processor 200 is used to process gait data to obtain gait speed data, gait frequency data, and gait cycle data, and is also used to obtain gait analysis data based on gait speed data, gait frequency data, and gait cycle data.

[0027] The aforementioned gait analysis and processing system based on time-space parameters includes a sensor group 100 comprising a triaxial accelerometer, a triaxial angular velocity sensor, a triaxial magnetometer, and a plantar pressure sensor. The triaxial accelerometer acquires data in the x, y, and z directions; the triaxial angular velocity sensor acquires data related to α, β, and γ; the triaxial magnetometer acquires data related to east-west, north-south, and sky-ground directions; and the plantar pressure sensor covers the entire sole of the foot and acquires plantar pressure-related data. The sampling frequency range is 20-100 Hz (10-50 ms). The data processor 200 has a built-in filtering module 201 and a coordinate system transformation module 202; The filtering module 201 is used to filter and clean the gait data. The posture of the foot in space changes during walking. The data collected by each sensor in real time is filtered and cleaned and then converted into a coordinate system on the horizontal plane. The coordinate system transformation module 202 is used to transform the coordinate system data of the gait data after filtering and cleaning.

[0028] A triaxial accelerometer and a triaxial angular velocity sensor are used to initially detect motion and acquire data. Dynamic Kalman filtering is used to calculate the posture of the insole or shoe, obtaining the current posture angle of the insole or shoe. Based on the posture angle, the acceleration data is processed in reverse to extract net acceleration and gravitational acceleration data in the same coordinate system. Pressure data, instantaneous pressure change data, and high-frequency acceleration change data are used to capture the landing phase during movement. The landing phase is used to initially divide the support and swing phases. Based on angular velocity data, angular velocity difference data, acceleration data, and posture data, combined with the gait cycle process state machine, detailed instantaneous gait in the support and swing phases is calculated. Based on the instantaneous gait and net acceleration, motion data in each direction in space is calculated. The percentage of single-leg gait cycle is statistically calculated based on the instantaneous gait. The real-time gait of both feet is calculated crosswise, and the movement and cycle data between steps are statistically analyzed.

[0029] A third embodiment, a third aspect, is an insole, wherein it includes a gait analysis and processing system based on time-space parameters as described in the second aspect.

[0030] Fourth embodiment, fourth aspect, a shoe, wherein it includes a gait analysis and processing system based on time-space parameters as described in the second aspect.

[0031] Fifth embodiment, fifth aspect, a chip, comprising: a processor for calling and running a computer program from a memory, causing a device on which the chip is mounted to perform: the method of the first aspect.

[0032] For example, memory may include random access memory, flash memory, read-only memory, programmable read-only memory, non-volatile memory, or registers, etc. The processor can be a central processing unit (CPU) or a graphics processing unit (GPU). The memory can store executable instructions. The processor can execute instructions stored in memory to implement the various processes described in this paper.

[0033] It is understood that the memory in this embodiment can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be ROM (Read-Only Memory), PROM (Programmable ROM), EPROM (Erasable PROM), EEPROM (Electrically Erasable EPROM), or flash memory.

[0034] Volatile memory can be RAM (Random Access Memory), which is used as an external cache; By way of example, but not limitation, many forms of RAM are available, such as SRAM (Static RAM), DRAM (Dynamic RAM), SDRAM (Synchronous DRAM), DDR SDRAM (Double Data Rate SDRAM), ESDRAM (Enhanced SDRAM), SLDRAM (Synchlink DRAM), and DRRAM (Direct Rambus RAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memory.

[0035] In some implementations, the memory stores elements such as upgrade packages, executable units, or data structures, or subsets thereof, or extended sets thereof: operating systems and applications; The operating system, which includes various system programs such as the framework layer, core library layer, and driver layer, is used to implement various basic business functions and handle hardware-based tasks. Applications, encompassing various applications, are used to implement various application services. Programs implementing the methods of the embodiments of this invention can be included within these applications.

[0036] Those skilled in the art will understand that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can implement the described functions in different ways for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0037] In the embodiments of this application, the disclosed systems, apparatus and methods can be implemented in other ways; For example, the division of units or modules is only a logical functional division, and there may be other division methods in actual implementation; For example, multiple units, modules, or components can be combined or integrated into another system; In addition, each functional unit or module in the embodiments of this application can be integrated into a processing unit or module, or it can exist as a separate physical entity, etc.

[0038] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0039] If the function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a machine-readable storage medium. Therefore, the technical solution of this application can be embodied in the form of a software product, which can be stored in a machine-readable storage medium and may include several instructions to cause an electronic device to execute all or part of the process of the technical solution described in the embodiments of this application. The aforementioned storage media may include various media capable of storing program code, such as ROM, RAM, removable disk, hard disk, magnetic disk, or optical disk.

[0040] In summary, the present invention provides a gait analysis processing method, system, insole, and shoe based on time-space parameters, which acquires gait data within multiple adjacent walking cycles; preprocesses the gait data to obtain gait classification data; processes the gait classification data to obtain gait speed data, gait frequency data, and gait cycle data, and obtains gait analysis data. It can acquire gait data in real time, accurately convert it into high-level gait analysis results, and obtain multi-dimensional prediction data for both feet, so as to realize real-time and accurate analysis and monitoring of the user's complex gait patterns.

[0041] The specific embodiments of the invention have been described above. It should be understood that the invention is not limited to the specific embodiments described above, and the devices and structures not described in detail should be understood to be implemented in a manner common to the art; those skilled in the art can make various modifications or alterations within the scope of the claims, and make several simple deductions, variations or substitutions, which do not affect the substantive content of the invention.

Claims

1. A gait analysis and processing method based on time-space parameters, characterized in that, Includes the following steps: Acquire gait data over multiple adjacent walking cycles; The gait data is preprocessed to obtain gait classification data; Based on the gait classification data, gait speed data, gait frequency data, and gait cycle data are obtained. Gait analysis data is obtained based on the gait speed data, the gait frequency data, and the gait cycle data.

2. The gait analysis and processing method based on time-space parameters according to claim 1, characterized in that, The method for obtaining the gait speed data based on the gait classification data is as follows: The ground contact time of the left and right feet in the gait data is recorded as t. Li With t Ri ; Calculate the step speed S for each adjacent step. i =L i / (t) Ri -t Li-1 ) or S i =L i / (t) Li+1 -t Ri ); The average step speed is calculated as S = ΣS i / Σ i .

3. The gait analysis and processing method based on time-space parameters according to claim 1, characterized in that, The method for obtaining the gait frequency data based on the gait classification data is as follows: The time of foot contact with the ground in the gait data is denoted as T. i ; The time it takes for adjacent left and right feet to alternate during walking is denoted as Δt. i =T i+1 -T i ; The step frequency during alternating steps of adjacent left and right feet during walking is denoted as f. i =1 / Δt i ; The average step frequency is calculated as F = Σf i / Σ i .

4. The gait analysis and processing method based on time-space parameters according to claim 1, characterized in that, The method for obtaining the gait cycle data based on the gait classification data is as follows: The time it takes for the left heel and right heel to land sequentially in the gait data is recorded as t. Li t Li+1 t Li+2 ... t Li+n With t Ri t Ri+1 t Ri+2 ... t Ri+n ; Calculate the gait period of the left foot in the i-th step as P. Li =t Li+1 -t Li ; Calculate the gait period of the right foot in the i-th step as P. Ri =t Ri+1 -t Ri ; Calculate the average gait period of the left foot as P. L =ΣP Li / Σ i ; Calculate the average gait period of the right foot as P. R =ΣP Ri / Σ i ; The average gait period of the left and right feet is calculated as P = (P L +P R ) / 2; The gait cycle data includes the gait cycle of the i-th step of the left foot, the gait cycle of the i-th step of the right foot, the average gait cycle of the left foot, the average gait cycle of the right foot, and the average gait cycle of both feet.

5. A gait analysis and processing system based on time-space parameters, characterized in that, It includes a sensor group and a data processor. The sensor group performs real-time detection and transmits the detected gait data to the data processor. The data processor is used to process the gait data to obtain gait speed data, gait frequency data, and gait cycle data, and is also used to obtain gait analysis data based on the gait speed data, the gait frequency data, and the gait cycle data.

6. The gait analysis and processing system based on time-space parameters according to claim 5, characterized in that, The sensor group includes a triaxial accelerometer, a triaxial angular velocity sensor, a triaxial magnetometer, and a plantar pressure sensor; The data processor has a built-in filtering module and a coordinate system transformation module; The filtering module is used to filter and clean the gait data; The coordinate system transformation module is used to transform the gait data after filtering and cleaning into coordinate system data.

7. An insole, characterized in that, It includes a gait analysis and processing system based on time-space parameters as described in any one of claims 5-6.

8. A shoe, characterized in that, It includes a gait analysis and processing system based on time-space parameters as described in any one of claims 5-6.

9. A chip, characterized in that, It includes: a processor for retrieving and running a computer program from memory, causing a device on which the chip is mounted to perform: the method as described in any one of claims 1-4.

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