Gait optimization prompting method, system and program product based on intelligent insole

Abstract

The application belongs to the technical field of motion analysis, and specifically discloses a gait optimization prompting method, system and program product based on an intelligent insole. The user walking monitoring data is collected through the intelligent insole, and the pressure distribution characteristics and walking dynamic characteristics are analyzed by using the user walking monitoring data, and then the gait standard degree and gait stability index calculation and gait optimization feedback are carried out, so as to integrate the precise gait analysis, real-time quantitative evaluation and personalized closed-loop intervention into one, and realize scientific and efficient gait detection and gait optimization guidance. The application can improve the accuracy and robustness of gait evaluation, can more finely identify gait problems, can guide the user in the walking process, realize dynamic adjustment and relearning of gait, and greatly expand the application boundary and beneficiary population of gait analysis technology.

Description

Technical Field

[0001] This invention belongs to the field of motion analysis technology, specifically relating to a gait optimization prompting method, system, and program product based on smart insoles. Background Technology

[0002] Gait is the posture and movement pattern of a person walking. A good gait is crucial for reducing joint stress, preventing sports injuries, and improving walking efficiency. Traditional gait analysis methods mostly rely on optical motion capture systems or force tables in laboratory environments. These devices are expensive and environmentally limited, making long-term monitoring and real-time intervention in everyday scenarios difficult. The emergence of smart insoles makes gait monitoring in everyday situations possible. A smart insole is a wearable device that embeds miniature sensors, electronic components, and wireless communication modules into a traditional insole, enabling it to sense, record, and transmit foot movement and physiological data in real time. While smart insoles can currently monitor users' foot movement and physiological data, there is a lack of effective technical means to match this data for real-time, accurate gait analysis, quantitative gait assessment, and personalized gait intervention. Summary of the Invention

[0003] The purpose of this invention is to provide a gait optimization prompting method, system, and program product based on smart insoles to solve the above-mentioned problems existing in the prior art.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, it provides a gait optimization prompt method based on smart insoles, including: Acquire pressure monitoring datasets and inertial measurement datasets collected by the smart insole during a user's gait cycle. The pressure monitoring datasets include pressure monitoring data at each monitoring point on both feet at each sampling time, and the inertial measurement datasets include foot rollover angular velocity and walking acceleration of both feet at each sampling time. The pressure distribution characteristics are determined based on the pressure monitoring dataset, including the length of the pressure center trajectory, the pressure ratio of the forefoot and hindfoot areas, and the pressure symmetry index of both feet. The walking dynamic characteristics are determined based on the inertial measurement dataset, including the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time. The gait standard index was calculated using the length of the pressure center trajectory, the pressure ratio of the forefoot and hindfoot areas, and the pressure symmetry index of both feet. The gait stability index was calculated using the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time. The system determines whether gait optimization prompts are needed based on the gait standardization index and / or gait stability index, and generates corresponding gait optimization prompt information when it is determined that gait optimization prompts are needed. The gait optimization prompts are output to the information prompt device on the smart insole, so that the information prompt device on the smart insole plays the gait optimization prompts.

[0005] In one possible design, determining the pressure distribution characteristics based on the pressure monitoring dataset includes: Based on the pressure monitoring dataset, the coordinates (x, y, y) of each monitoring point on a single foot were determined. i y i ), i represents the monitoring point number, and p represents the pressure monitoring data of each monitoring point on a single foot at each sampling time. i (t), where t represents the sampling time; Based on the coordinates of each monitoring point on a single foot (x i y i ) and pressure monitoring data p at each monitoring point on a single foot at each sampling time. i (t), calculate the pressure center coordinates of the single foot at each sampling time [COP] x (t), COP y (t)]:

[0006] Where N represents the number of monitoring points for a single foot; Using the pressure center coordinates of a single foot at each sampling time [COP] x (t), COP y [(t)] Calculate the pressure center transfer length C of a single foot within one gait cycle of the user. COP :

[0007] Where k represents the sampling time sequence number, t k The sampling time t represents the sampling time with index k. k+1 The sampling time with index k+1 is represented, and M represents the number of sampling times within one gait cycle of the user. The length C of the shift in the center of pressure of both feet during one gait cycle of the user. COP The average value is taken to obtain the length of the pressure center trajectory.

[0008] In one possible design, determining the pressure distribution characteristics based on the pressure monitoring dataset includes: Determine the set of monitoring points I for the forefoot region of a single foot. f And the set of monitoring points in the hindfoot area I r ; Calculate the total pressure P in the forefoot region of a single foot during one gait cycle of a user. f And the total pressure P in the hindfoot area r : ; Using the total pressure P of the forefoot area of ​​a single foot during a user's gait cycle f And the total pressure P in the hindfoot area r Calculate the sum of pressure R between the forefoot and hindfoot regions of a single foot. P R P =P f / P r ; The sum of the forefoot and hindfoot pressures of both feet during a user's gait cycle is compared to R. P The average value is taken to obtain the pressure ratio between the forefoot and hindfoot areas.

[0009] In one possible design, determining the pressure distribution characteristics based on the pressure monitoring dataset includes: Calculate the total pressure P on a single foot during one gait cycle of the user, P = P f +P r ; The difference in total pressure between the two feet, ΔP, and the sum of the total pressures, P, are determined based on the calculation results of the total pressure from both feet. 总 ; Using the difference in total pressure ΔP and the sum of total pressure P 总 Calculate the bipedal pressure symmetry index S P S P =1-(|ΔP| / P 总 ).

[0010] In one possible design, determining the walking dynamics based on the inertial measurement dataset includes: The foot rotation angular velocity at each sampling moment is subtracted from the foot rotation angular velocity at the previous sampling moment to obtain the angular velocity difference. The angular velocity difference is then divided by the sampling time interval to obtain the rate of change of the angular velocity of the two feet at each sampling moment. The average of the rate of change of the angular velocity of the two feet at each sampling moment is then taken to obtain the rate of change of the foot rotation angular velocity at each sampling moment. The acceleration difference is obtained by subtracting the walking acceleration of the previous sampling time from the walking acceleration of both feet at each sampling time. The acceleration difference is then divided by the sampling time interval to obtain the rate of change of acceleration of both feet at each sampling time. The average of the rate of change of acceleration of both feet at each sampling time is then taken to obtain the walking acceleration rate of change at each sampling time.

[0011] In one possible design, the calculation of the gait standard index using the pressure center trajectory length, the pressure ratio between the forefoot and hindfoot areas, and the bipedal pressure symmetry index includes: The gait standard index is calculated by substituting the pressure center trajectory length, the pressure ratio between the forefoot and hindfoot areas, and the bipedal pressure symmetry index into a preset gait standard index formula. The gait standard index formula is as follows:

[0012] Among them, G s S is the gait standard index. P R is the bipedal pressure symmetry index, where R is the pressure ratio between the forefoot and hindfoot regions. ref The set reference pressure ratio, C is the length of the pressure center trajectory, C ref As the reference trajectory length, ω1, ω2 and ω3 are the set first weight coefficient, second weight coefficient and third weight coefficient, respectively, and ω1+ω2+ω3=1.

[0013] In one possible design, the calculation of the gait stability index using the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time includes: The rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time are substituted into a preset gait stability index formula for calculation to obtain the gait stability index. The gait stability index formula is as follows:

[0014] Among them, G t The gait stability index is defined as k, where k represents the sampling time number, M represents the number of sampling times within one gait cycle of the user, and t represents the gait stability index. k Characterizing the sampling time with index k, Δθ(t) k (t) represents the sampling time. k The rate of change of foot roll angular velocity, Δσ(t) k (t) represents the sampling time. k The rate of change of walking acceleration, where α and β are the set weights for the angular velocity and acceleration components, respectively.

[0015] Secondly, a gait optimization prompting system based on smart insoles is provided, including a data acquisition unit, a feature extraction unit, an index calculation unit, a gait determination unit, and a prompt output unit, wherein: The data acquisition unit is used to acquire the pressure monitoring dataset and inertial measurement dataset collected by the smart insole during one gait cycle of the user. The pressure monitoring dataset includes pressure monitoring data of each monitoring point on both feet at each sampling time, and the inertial measurement dataset includes the foot rollover angular velocity and walking acceleration of both feet at each sampling time. The feature extraction unit is used to determine pressure distribution features based on the pressure monitoring dataset. The pressure distribution features include the length of the pressure center trajectory, the pressure ratio of the forefoot and hindfoot areas, and the pressure symmetry index of both feet. The unit also determines walking dynamic features based on the inertial measurement dataset. The walking dynamic features include the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time. The index calculation unit is used to calculate the gait standard index using the pressure center trajectory length, the pressure ratio of the forefoot and hindfoot areas and the pressure symmetry index of both feet, and to calculate the gait stability index using the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time. The gait determination unit is used to determine whether gait optimization prompts are needed based on the gait standardity index and / or gait stability index, and to generate corresponding gait optimization prompt information when it is determined that gait optimization prompts are needed. The prompt output unit is used to output gait optimization prompt information to the information prompt device on the smart insole, so that the information prompt device on the smart insole can play the gait optimization prompt information.

[0016] Thirdly, it provides a gait optimization prompting system based on smart insoles, including: Memory, used to store instructions; The processor is configured to read instructions stored in the memory and execute any one of the gait optimization prompting methods based on smart insoles described in the first aspect above, according to the instructions.

[0017] Fourthly, a computer-readable storage medium is provided, on which instructions are stored, which, when executed on a computer, cause the computer to perform any one of the gait optimization prompting methods based on smart insoles described in the first aspect. Simultaneously, a computer program product is also provided, which, when executed on a computer, performs any one of the gait optimization prompting methods based on smart insoles described in the first aspect.

[0018] Beneficial Effects: This invention collects user walking monitoring data through smart insoles and analyzes pressure distribution and walking dynamic characteristics using this data. It then calculates gait standardization and stability indices, and provides gait optimization feedback. This integrates precise gait analysis, real-time quantitative assessment, and personalized closed-loop intervention to achieve scientific and efficient gait detection and optimization guidance. This invention improves the accuracy and robustness of gait assessment, enabling more precise identification of complex gait problems such as mild limping, inversion / pronation abnormalities, and uneven impact load. It allows users to receive guidance during walking, achieving dynamic adjustment and relearning of gait. Simultaneously, it provides efficient and feasible technical support for posture correction in athletes, fall prevention in the elderly, home rehabilitation for postoperative patients, and daily health management for the general public, greatly expanding the application boundaries and beneficiary population of gait analysis technology. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the method in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the system configuration in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the system configuration in Embodiment 3 of the present invention. Detailed Implementation

[0021] It should be noted that the descriptions of these embodiments are intended to aid in understanding the invention and do not constitute a limitation thereof. The specific structural and functional details disclosed herein are merely for describing exemplary embodiments of the invention. However, the invention may be embodied in many alternative forms and should not be construed as being limited to the embodiments described herein.

[0022] It should be understood that, unless otherwise explicitly specified and limited, the corresponding terms should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments according to the specific circumstances.

[0023] Specific details are provided in the following description to provide a complete understanding of the exemplary embodiments. However, those skilled in the art will understand that the exemplary embodiments can be implemented without these specific details. For example, the system may be shown in block diagrams to avoid obscuring the example with unnecessary details. In other embodiments, well-known processes, structures, and techniques may be shown without non-essential details to avoid obscuring the embodiments.

[0024] Example 1: This embodiment provides a gait optimization prompting method based on smart insoles, which can be applied to corresponding smart insole controllers. For example... Figure 1 As shown, the method includes the following steps: S1. Acquire the pressure monitoring dataset and inertial measurement dataset collected by the smart insole during one gait cycle of the user. The pressure monitoring dataset includes pressure monitoring data of each monitoring point on both feet at each sampling time. The inertial measurement dataset includes the foot rollover angular velocity and walking acceleration of both feet at each sampling time.

[0025] In practical implementation, inertial measurement units and pressure sensor arrays can be installed on the smart insoles of both feet, with the pressure sensor array deployed at predetermined coordinate points. During walking, the inertial measurement units on the smart insoles collect inertial measurement data of the user's feet within one gait cycle and transmit this data to the smart insole controller. Similarly, the pressure sensor array on the smart insole collects pressure monitoring data of the user's feet within one gait cycle and transmits this data to the smart insole controller. The pressure monitoring dataset contains pressure data at each monitoring point on both feet at each sampling time, while the inertial measurement dataset contains the foot roll angular velocity and walking acceleration at each sampling time.

[0026] S2. Determine the pressure distribution characteristics based on the pressure monitoring dataset. The pressure distribution characteristics include the length of the pressure center trajectory, the pressure ratio between the forefoot and hindfoot areas, and the pressure symmetry index of both feet. Determine the walking dynamic characteristics based on the inertial measurement dataset. The walking dynamic characteristics include the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time.

[0027] In practical implementation, the intelligent insole controller can extract pressure distribution features based on the pressure monitoring dataset. These features include the length of the pressure center trajectory, the pressure ratio between the forefoot and hindfoot areas, and the pressure symmetry index between the two feet. The extraction process includes: Based on the pressure monitoring dataset, the coordinates (x, y, y) of each monitoring point on a single foot were determined. i y i ), i represents the monitoring point number, and p represents the pressure monitoring data of each monitoring point on a single foot at each sampling time. i (t), where t represents the sampling time; Based on the coordinates of each monitoring point on a single foot (x i y i ) and pressure monitoring data p at each monitoring point on a single foot at each sampling time. i (t), calculate the pressure center coordinates of the single foot at each sampling time [COP] x (t), COP y (t)]:

[0028] Where N represents the number of monitoring points for a single foot; Using the pressure center coordinates of a single foot at each sampling time [COP] x (t), COP y [(t)] Calculate the pressure center transfer length C of a single foot within one gait cycle of the user. COP :

[0029] Where k represents the sampling time sequence number, t k The sampling time t represents the sampling time with index k. k+1 The sampling time with index k+1 is represented, and M represents the number of sampling times within one gait cycle of the user. The length C of the shift in the center of pressure of both feet during one gait cycle of the user. COP The average value is taken to obtain the length of the pressure center trajectory.

[0030] And, determine the set of monitoring points I for the forefoot region of a single foot. f And the set of monitoring points in the hindfoot area I r ; Calculate the total pressure P in the forefoot region of a single foot during one gait cycle of a user. f And the total pressure P in the hindfoot area r : ; Using the total pressure P of the forefoot area of ​​a single foot during a user's gait cycle f And the total pressure P in the hindfoot area r Calculate the sum of pressure R between the forefoot and hindfoot regions of a single foot. P R P =P f / P r ; The sum of the forefoot and hindfoot pressures of both feet during a user's gait cycle is compared to R. P The average value is taken to obtain the pressure ratio between the forefoot and hindfoot areas.

[0031] And, calculate the total pressure P on a single foot during one gait cycle of the user, P=P f +P r ; The difference in total pressure between the two feet, ΔP, and the sum of the total pressures, P, are determined based on the calculation results of the total pressure from both feet. 总 ; Using the difference in total pressure ΔP and the sum of total pressure P 总 Calculate the bipedal pressure symmetry index S P S P =1-(|ΔP| / P 总 ).

[0032] Meanwhile, the intelligent insole controller can extract walking dynamic features based on the inertial measurement dataset. These walking dynamic features include the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time. The extraction process includes: The foot rotation angular velocity at each sampling moment is subtracted from the foot rotation angular velocity at the previous sampling moment to obtain the angular velocity difference. The angular velocity difference is then divided by the sampling time interval to obtain the rate of change of the angular velocity of the two feet at each sampling moment. The average of the rate of change of the angular velocity of the two feet at each sampling moment is then taken to obtain the rate of change of the foot rotation angular velocity at each sampling moment. The acceleration difference is obtained by subtracting the walking acceleration of the previous sampling time from the walking acceleration of both feet at each sampling time. The acceleration difference is then divided by the sampling time interval to obtain the rate of change of acceleration of both feet at each sampling time. The average of the rate of change of acceleration of both feet at each sampling time is then taken to obtain the walking acceleration rate of change at each sampling time.

[0033] S3. Calculate the gait standard index using the length of the pressure center trajectory, the pressure ratio of the forefoot and hindfoot areas, and the pressure symmetry index of both feet. Calculate the gait stability index using the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time.

[0034] In practical implementation, the intelligent insole controller can substitute the pressure center trajectory length, the pressure ratio between the forefoot and hindfoot areas, and the pressure symmetry index of both feet into a preset gait standard index formula to calculate the gait standard index. The gait standard index formula is as follows:

[0035] Among them, G s S is the gait standard index. P R is the bipedal pressure symmetry index, where R is the pressure ratio between the forefoot and hindfoot regions. ref The set reference pressure ratio, C is the length of the pressure center trajectory, C ref As the reference trajectory length, ω1, ω2 and ω3 are the set first weight coefficient, second weight coefficient and third weight coefficient, respectively, and ω1+ω2+ω3=1.

[0036] Simultaneously, the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time can be substituted into a preset gait stability index formula for calculation to obtain the gait stability index. The gait stability index formula is as follows:

[0037] Among them, G t The gait stability index is defined as k, where k represents the sampling time number, M represents the number of sampling times within one gait cycle of the user, and t represents the gait stability index. k Characterizing the sampling time with index k, Δθ(t) k (t) represents the sampling time. k The rate of change of foot roll angular velocity, Δσ(t) k (t) represents the sampling time. kThe rate of change of walking acceleration, where α and β are the set weights for the angular velocity and acceleration components, respectively.

[0038] S4. Determine whether gait optimization prompts are needed based on the gait standardization index and / or gait stability index, and generate corresponding gait optimization prompt information when it is determined that gait optimization prompts are needed.

[0039] In practical implementation, the intelligent insole controller can be based on the gait standard index G. s and / or gait stability index G t Determine whether gait optimization prompts are needed. If the gait standardization index G... s If the gait stability index falls below the set threshold, the smart insole controller can generate corresponding gait optimization prompts, such as "Pay attention to walking posture, keep your body upright." t If the gait stability index falls below the set threshold, the smart insole controller can generate corresponding gait optimization prompts, such as "reduce cadence and increase support surface".

[0040] S5. Output gait optimization prompts to the information prompt device on the smart insole, so that the information prompt device on the smart insole plays the gait optimization prompts.

[0041] In practice, the smart insole controller outputs gait optimization prompts to an information prompting device on the smart insole, such as a voice player, so that the information prompting device can play the gait optimization prompts to provide gait optimization prompts and guidance to the user.

[0042] This method can improve the accuracy and robustness of gait assessment, and can more precisely identify complex gait problems such as mild limping, inversion / exversion abnormalities, and uneven impact load. It allows users to receive guidance during walking, enabling dynamic adjustment and relearning of gait. At the same time, it provides efficient and feasible technical support for posture correction in sports, fall prevention in the elderly, home rehabilitation for postoperative patients, and daily health management for the general public, greatly expanding the application boundaries and beneficiaries of gait analysis technology.

[0043] Example 2: This embodiment provides a gait optimization prompting system based on smart insoles, such as... Figure 2 As shown, it includes a data acquisition unit, a feature extraction unit, an index calculation unit, a gait determination unit, and a prompt output unit, wherein: The data acquisition unit is used to acquire the pressure monitoring dataset and inertial measurement dataset collected by the smart insole during one gait cycle of the user. The pressure monitoring dataset includes pressure monitoring data of each monitoring point on both feet at each sampling time, and the inertial measurement dataset includes the foot rollover angular velocity and walking acceleration of both feet at each sampling time. The feature extraction unit is used to determine pressure distribution features based on the pressure monitoring dataset. The pressure distribution features include the length of the pressure center trajectory, the pressure ratio of the forefoot and hindfoot areas, and the pressure symmetry index of both feet. The unit also determines walking dynamic features based on the inertial measurement dataset. The walking dynamic features include the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time. The index calculation unit is used to calculate the gait standard index using the pressure center trajectory length, the pressure ratio of the forefoot and hindfoot areas and the pressure symmetry index of both feet, and to calculate the gait stability index using the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time. The gait determination unit is used to determine whether gait optimization prompts are needed based on the gait standardity index and / or gait stability index, and to generate corresponding gait optimization prompt information when it is determined that gait optimization prompts are needed. The prompt output unit is used to output gait optimization prompt information to the information prompt device on the smart insole, so that the information prompt device on the smart insole can play the gait optimization prompt information.

[0044] Example 3: This embodiment provides a gait optimization prompting system based on smart insoles, such as... Figure 3 As shown, at the hardware level, it includes: The data interface is used to establish data communication between the processor and external data terminals; Memory, used to store instructions; The processor is used to read instructions stored in the memory and execute the gait optimization prompting method based on smart insoles in Embodiment 1 according to the instructions.

[0045] Optionally, the system also includes an internal bus, through which the processor, memory, and data interface can be interconnected. This internal bus can be a PCIe (Peripheral Component Interconnect Eexpress) bus, which can be divided into an address bus, a data bus, a control bus, etc. The memory can include, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Flash Memory, First Input First Output (FIFO), and / or First In Last Out (FILO). The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0046] Example 4: This embodiment provides a computer-readable storage medium storing instructions. When these instructions are executed on a computer, the computer performs the gait optimization prompting method based on smart insoles as described in Embodiment 1. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0047] This embodiment also provides a computer program product that, when run on a computer, executes the gait optimization prompting method based on smart insoles described in Embodiment 1. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0048] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A gait optimization prompting method based on smart insoles, characterized in that, include: Acquire pressure monitoring datasets and inertial measurement datasets collected by the smart insole during a user's gait cycle. The pressure monitoring datasets include pressure monitoring data at each monitoring point on both feet at each sampling time, and the inertial measurement datasets include foot rollover angular velocity and walking acceleration of both feet at each sampling time. The pressure distribution characteristics are determined based on the pressure monitoring dataset, including the length of the pressure center trajectory, the pressure ratio of the forefoot and hindfoot areas, and the pressure symmetry index of both feet. The walking dynamic characteristics are determined based on the inertial measurement dataset, including the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time. The gait standard index was calculated using the length of the pressure center trajectory, the pressure ratio of the forefoot and hindfoot areas, and the pressure symmetry index of both feet. The gait stability index was calculated using the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time. The system determines whether gait optimization prompts are needed based on the gait standardization index and / or gait stability index, and generates corresponding gait optimization prompt information when it is determined that gait optimization prompts are needed. The gait optimization prompts are output to the information prompt device on the smart insole, so that the information prompt device on the smart insole plays the gait optimization prompts.

2. The gait optimization prompting method based on smart insoles according to claim 1, characterized in that, The step of determining pressure distribution characteristics based on pressure monitoring datasets includes: Based on the pressure monitoring dataset, the coordinates (x, y, y) of each monitoring point on a single foot were determined. i y i ), i represents the monitoring point number, and p represents the pressure monitoring data of each monitoring point on a single foot at each sampling time. i (t), where t represents the sampling time; Based on the coordinates of each monitoring point on a single foot (x i y i ) and pressure monitoring data p at each monitoring point on a single foot at each sampling time. i (t), calculate the pressure center coordinates of the single foot at each sampling time [COP] x (t), COP y (t)]: Where N represents the number of monitoring points for a single foot; Using the pressure center coordinates of a single foot at each sampling time [COP] x (t), COP y [(t)] Calculate the pressure center transfer length C of a single foot within one gait cycle of the user. COP : Where k represents the sampling time sequence number, t k The sampling time t represents the sampling time with index k. k+1 The sampling time with index k+1 is represented, and M represents the number of sampling times within one gait cycle of the user. The length C of the shift in the center of pressure of both feet during one gait cycle of the user. COP The average value is taken to obtain the length of the pressure center trajectory.

3. The gait optimization prompting method based on smart insoles according to claim 2, characterized in that, The step of determining pressure distribution characteristics based on pressure monitoring datasets includes: Determine the set of monitoring points I for the forefoot region of a single foot. f And the set of monitoring points in the hindfoot area I r ; Calculate the total pressure P in the forefoot region of a single foot during one gait cycle of a user. f And the total pressure P in the hindfoot area r : ； Using the total pressure P of the forefoot area of ​​a single foot during a user's gait cycle f And the total pressure P in the hindfoot area r Calculate the sum of pressure R between the forefoot and hindfoot regions of a single foot. P R P =P f / P r ; The sum of the forefoot and hindfoot pressures of both feet during a user's gait cycle is compared to R. P The average value is taken to obtain the pressure ratio between the forefoot and hindfoot areas.

4. The gait optimization prompting method based on smart insoles according to claim 3, characterized in that, The step of determining pressure distribution characteristics based on pressure monitoring datasets includes: Calculate the total pressure P on a single foot during one gait cycle of the user, P = P f +P r ; The difference in total pressure between the two feet, ΔP, and the sum of the total pressures, P, are determined based on the calculation results of the total pressure from both feet. 总 ; Using the difference in total pressure ΔP and the sum of total pressure P 总 Calculate the bipedal pressure symmetry index S P S P =1-(|ΔP| / P 总 ).

5. The gait optimization prompting method based on smart insoles according to claim 1, characterized in that, The determination of walking dynamic characteristics based on the inertial measurement dataset includes: The foot rotation angular velocity at each sampling moment is subtracted from the foot rotation angular velocity at the previous sampling moment to obtain the angular velocity difference. The angular velocity difference is then divided by the sampling time interval to obtain the rate of change of the angular velocity of the two feet at each sampling moment. The average of the rate of change of the angular velocity of the two feet at each sampling moment is then taken to obtain the rate of change of the foot rotation angular velocity at each sampling moment. The acceleration difference is obtained by subtracting the walking acceleration of the previous sampling time from the walking acceleration of both feet at each sampling time. The acceleration difference is then divided by the sampling time interval to obtain the rate of change of acceleration of both feet at each sampling time. The average of the rate of change of acceleration of both feet at each sampling time is then taken to obtain the walking acceleration rate of change at each sampling time.

6. The gait optimization prompting method based on smart insoles according to claim 1, characterized in that, The calculation of the gait standard index using the length of the pressure center trajectory, the pressure ratio between the forefoot and hindfoot areas, and the bipedal pressure symmetry index includes: The gait standard index is calculated by substituting the pressure center trajectory length, the pressure ratio between the forefoot and hindfoot areas, and the bipedal pressure symmetry index into a preset gait standard index formula. The gait standard index formula is as follows: Among them, G s S is the gait standard index. P R is the bipedal pressure symmetry index, where R is the pressure ratio between the forefoot and hindfoot regions. ref The set reference pressure ratio, C is the length of the pressure center trajectory, C ref As the reference trajectory length, ω1, ω2 and ω3 are the set first weight coefficient, second weight coefficient and third weight coefficient, respectively, and ω1+ω2+ω3=1.

7. The gait optimization prompting method based on smart insoles according to claim 1, characterized in that, The calculation of the gait stability index using the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time includes: The rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time are substituted into a preset gait stability index formula for calculation to obtain the gait stability index. The gait stability index formula is as follows: Among them, G t The gait stability index is defined as k, where k represents the sampling time number, M represents the number of sampling times within one gait cycle of the user, and t represents the gait stability index. k Characterizing the sampling time with index k, Δθ(t) k (t) represents the sampling time. k The rate of change of foot roll angular velocity, Δσ(t) k (t) represents the sampling time. k The rate of change of walking acceleration, where α and β are the set weights for the angular velocity and acceleration components, respectively.

8. A gait optimization prompting system based on smart insoles, characterized in that, It includes a data acquisition unit, a feature extraction unit, an index calculation unit, a gait determination unit, and a prompt output unit, wherein: The data acquisition unit is used to acquire the pressure monitoring dataset and inertial measurement dataset collected by the smart insole during one gait cycle of the user. The pressure monitoring dataset includes pressure monitoring data of each monitoring point on both feet at each sampling time, and the inertial measurement dataset includes the foot rollover angular velocity and walking acceleration of both feet at each sampling time. The feature extraction unit is used to determine pressure distribution features based on the pressure monitoring dataset. The pressure distribution features include the length of the pressure center trajectory, the pressure ratio of the forefoot and hindfoot areas, and the pressure symmetry index of both feet. The unit also determines walking dynamic features based on the inertial measurement dataset. The walking dynamic features include the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time. The index calculation unit is used to calculate the gait standard index using the pressure center trajectory length, the pressure ratio of the forefoot and hindfoot areas and the pressure symmetry index of both feet, and to calculate the gait stability index using the rate of change of foot rollover angular velocity and the rate of change of walking acceleration at each sampling time. The gait determination unit is used to determine whether gait optimization prompts are needed based on the gait standardity index and / or gait stability index, and to generate corresponding gait optimization prompt information when it is determined that gait optimization prompts are needed. The prompt output unit is used to output gait optimization prompt information to the information prompt device on the smart insole, so that the information prompt device on the smart insole can play the gait optimization prompt information.

9. A gait optimization prompting system based on smart insoles, characterized in that, include: Memory, used to store instructions; A processor is configured to read instructions stored in the memory and execute the gait optimization prompting method based on smart insoles as described in any one of claims 1-7 according to the instructions.

10. A computer program product, characterized in that, When the computer program product is run on a computer, it executes the gait optimization prompting method based on smart insoles as described in any one of claims 1-7.

Images