Gait intelligence monitoring method, system, device, medium and program product

Abstract

The application provides a gait intelligent monitoring method, system, device, medium and program product. The method comprises the following steps: based on multi-pose positioning, synchronously collecting pressure data and center of gravity dynamic data of different regions of the sole of a target object; performing denoising processing on the synchronously collected pressure data and center of gravity dynamic data; performing multi-dimensional data fusion on the denoised pressure data and center of gravity dynamic data based on gait cycle, spatial distribution and dynamic characteristics, and generating comprehensive gait data containing sole pressure distribution parameters and center of gravity dynamic balance parameters through fusion; transmitting the obtained comprehensive gait data to an evaluation terminal; selecting an evaluation model adapted to the multi-pose positioning based on the evaluation terminal, evaluating and identifying the comprehensive gait data through the evaluation model, and obtaining a gait recognition result. The embodiment of the application is based on multi-pose standardized positioning, and is adapted to evaluation of different evaluation scenes. The pressure data and gravity dynamic data are fused to perform gait monitoring, and the monitoring accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of information monitoring technology, specifically to a gait intelligent monitoring method, system, device, medium, and program product. Background Technology

[0002] Sarcopenia is a syndrome closely related to aging, characterized by a decrease in skeletal muscle mass, strength, and function. Falls are a common safety risk among the elderly, and both skeletal muscle mass and strength significantly impact their quality of life and safety. Therefore, sarcopenia assessment and fall prevention risk assessment are essential, and accurate monitoring of gait indicators is crucial for early intervention and risk warning.

[0003] The core indicators for assessing the severity of sarcopenia and fall risk are gait parameters (stances with feet together, front-leg stance, and rear-leg stance) and central balance. For fall prevention assessment, a front-leg stance is preferred, while for sarcopenia muscle strength assessment, a side-by-side stance is preferred.

[0004] Currently, gait monitoring technologies are mainly divided into two categories: one is a monitoring scheme based on a single pressure sensor, which collects pressure distribution data through a plantar pressure sensor, but cannot effectively capture the dynamic changes in the weight of the left and right feet, resulting in insufficient accuracy in the assessment of center of gravity; the other is a monitoring scheme based on gravity sensing (accelerometers, gyroscopes, etc.), which can detect the asymmetry of the center of gravity between the left and right feet, but lacks refined data support from plantar pressure, and is insufficient to fully reflect the integrity and symmetry of gait. In addition, the existing monitoring devices are mostly designed with a single parallel stance, which cannot guide the person being assessed to perform standardized positioning in multiple postures, resulting in a lack of consistency in gait data under different assessment scenarios, which affects the reliability of the assessment results and the reliable comparability of the same sample.

[0005] Therefore, there is an urgent need for a gait monitoring technology that can integrate the advantages of pressure sensing and gravity sensing and has a standardized positioning design. This technology can be applied to different assessment scenarios such as sarcopenia assessment and fall prevention risk assessment, while solving the problems of insufficient monitoring accuracy and non-standard positioning in existing technologies.

[0006] The content in the background section merely discloses technology known only to the inventors and is not intended to represent prior art in the field. Summary of the Invention

[0007] This application aims to provide a gait intelligent monitoring method, system, device, medium, and program product to solve at least one problem existing in the prior art.

[0008] According to a first aspect of this application, a gait intelligent monitoring method is provided, comprising: Based on multi-posture positioning, pressure data and dynamic center of gravity data of different areas of the target object's foot are collected simultaneously. The pressure data and center of gravity dynamic data collected synchronously are denoised using a Kalman filter algorithm. The denoised pressure data and center-of-gravity dynamic data are fused in multiple dimensions based on gait cycle, spatial distribution, and dynamic characteristics to generate comprehensive gait data that includes plantar pressure distribution parameters and center-of-gravity dynamic balance parameters, including: Establish a spatiotemporal correlation between pressure data and center of gravity dynamic data, and based on the needs of gait analysis, integrate the scattered pressure data and center of gravity dynamic data into a pressure feature set and a center of gravity dynamic balance feature set with gait analysis value; the pressure feature set includes total plantar pressure and the ratio of left to right plantar pressure, and the center of gravity dynamic balance feature set includes center of gravity position features, center of gravity sway angle, and dynamic center of gravity velocity features; the center of gravity position features include center of gravity offset, and the dynamic center of gravity velocity features include dynamic center of gravity velocity amplitude. Based on the acquired pressure feature set and center of gravity dynamic balance feature set, the center of gravity and pressure are jointly fused to obtain comprehensive gait data, which includes: Balance Index: This is the core fusion data, obtained by multiplying the center of gravity offset and the left-right pressure ratio of the foot, and is used to comprehensively reflect the gait balance status. Gait stability coefficient, which reflects gait stability, is obtained by combining total pressure and center of gravity sway angle; Sway Index: A comprehensive reflection of static balance ability, obtained based on the sway angle and center of gravity offset; Dynamic stability coefficient: used to evaluate dynamic balance during walking, based on center of gravity offset and dynamic center of gravity velocity amplitude; The acquired comprehensive gait data is transmitted to the evaluation terminal; Based on the evaluation model for terminal selection and multi-posture positioning adaptation, the comprehensive gait data is evaluated and identified through the evaluation model to obtain gait recognition results.

[0009] In some specific embodiments, the pressure feature set also includes the heel / forefoot ratio and peak pressure; the center of gravity dynamic balance feature set also includes dynamic center of gravity acceleration; and the center of gravity position feature also includes measured center of gravity coordinate values ​​and center of gravity offset ratio.

[0010] In some specific embodiments, the dynamic balance feature set of the center of gravity further includes dynamic center of gravity angular velocity, and the comprehensive gait data further includes: Balance margin: It is characterized by the anti-tipping ability of the plantar support surface, which is obtained based on the width of the plantar support surface and the dynamic center of gravity angular velocity.

[0011] In some specific embodiments, a spatiotemporal correlation is established between pressure data and center of gravity dynamic data, and based on the needs of gait analysis, the dispersed pressure data and center of gravity dynamic data are integrated into a pressure feature set and a center of gravity dynamic balance feature set with gait analysis value, including: Data alignment: Based on a preset sampling frequency, strict timestamp matching is ensured. Timestamp difference verification is used to ensure that pressure data and center of gravity dynamic data are completely synchronized in time and space, avoiding misalignment and fusion; the sampling frequency is ≥100Hz. Feature extraction: Based on aligned data, extract pressure feature sets and center of gravity dynamic balance feature sets with gait analysis value from denoised pressure data and center of gravity dynamic data.

[0012] In some specific embodiments, the multi-posture positioning includes the target object selecting a corresponding position based on standardized position markers according to the evaluation requirements, maintaining the selected position to complete a preset action, and the positioning includes: a straight position with the right foot in front of the left foot, a position with the left and right feet side by side, and a position with the right foot in front of the left foot. The standardized position markers are set on the surface of the monitoring pad.

[0013] In some specific embodiments, the pressure data includes pressure values, pressure distribution patterns, and pressure change rates in different areas of the sole of the foot, which are collected in real time using a flexible thin-film pressure sensing module. The center of gravity dynamic data includes the three-dimensional displacement, acceleration, and angular velocity data of the body's center of gravity, which are captured in real time using a gravity sensing module.

[0014] According to a second aspect of this application, a gait intelligent monitoring system is provided, comprising: The data acquisition module is configured to simultaneously acquire pressure data and dynamic center of gravity data of different areas of the sole of the target object based on multi-posture positioning; the data acquisition module includes a flexible film pressure sensing module, a gravity sensing module, and a monitoring pad with standardized station markings, wherein the flexible film pressure sensing module and the gravity sensing module are both set on the monitoring pad. The data processing module is configured to denoise the synchronously acquired pressure data and center of gravity dynamic data, and uses the Kalman filter algorithm to denoise the pressure data and center of gravity dynamic data. The multi-dimensional data fusion module is configured to perform multi-dimensional data fusion on denoised pressure data and center of gravity dynamic data based on gait cycle, spatial distribution, and dynamic characteristics, generating comprehensive gait data including plantar pressure distribution parameters and center of gravity dynamic balance parameters, including: Establish a spatiotemporal correlation between pressure data and center of gravity dynamic data, and based on the needs of gait analysis, integrate the scattered pressure data and center of gravity dynamic data into a pressure feature set and a center of gravity dynamic balance feature set with gait analysis value; the pressure feature set includes total plantar pressure and the ratio of left to right plantar pressure, and the center of gravity dynamic balance feature set includes center of gravity position features, center of gravity sway angle, and dynamic center of gravity velocity features; the center of gravity position features include center of gravity offset, and the dynamic center of gravity velocity features include dynamic center of gravity velocity amplitude. Based on the acquired pressure feature set and center of gravity dynamic balance feature set, the center of gravity and pressure are jointly fused to obtain comprehensive gait data, which includes: Balance Index: This is the core fusion data, obtained by multiplying the center of gravity offset and the left-right pressure ratio of the foot, and is used to comprehensively reflect the gait balance status. Gait stability coefficient, which reflects gait stability, is obtained by combining total pressure and center of gravity sway angle; Sway Index: A comprehensive reflection of static balance ability, obtained based on the sway angle and center of gravity offset; Dynamic stability coefficient: used to evaluate dynamic balance during walking, based on center of gravity offset and dynamic center of gravity velocity amplitude; A transmission module configured to transmit the acquired integrated gait data to an evaluation terminal; The evaluation and recognition module is configured to select an evaluation model based on the evaluation terminal and adapt it to multiple postures, and to evaluate and recognize the comprehensive gait data through the evaluation model to obtain gait recognition results.

[0015] According to a third aspect of this application, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method described in any of the above embodiments.

[0016] According to a fourth aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the method described in any of the above embodiments.

[0017] According to a fifth aspect of this application, a program product is provided, comprising a computer program, wherein the computer program, when executed by a processor, implements any of the methods described in the above embodiments.

[0018] Based on the above embodiments of this application, the beneficial effects of this application include one or more of the following effects in combination: In summary, the embodiments of this application are based on multi-posture standardized positioning, and simultaneously collect pressure data and dynamic center of gravity data of different areas of the target object's sole, adapting to different assessment scenarios such as sarcopenia assessment and fall prevention risk assessment; Furthermore, gait monitoring is improved by integrating pressure data with dynamic gravity data. Further, the denoised pressure data and center-of-gravity dynamic data are fused in multiple dimensions based on gait cycle, spatial distribution, and dynamic characteristics to generate comprehensive gait data including plantar pressure distribution parameters and center-of-gravity dynamic balance parameters. This includes establishing the spatiotemporal correlation between pressure data and center-of-gravity dynamic data, and integrating the dispersed pressure data and center-of-gravity dynamic data into a pressure feature set and a center-of-gravity dynamic balance feature set with gait analysis value, based on gait analysis needs. The pressure feature set includes total plantar pressure and the left-right plantar pressure ratio; the center-of-gravity dynamic balance feature set includes center-of-gravity position characteristics, center-of-gravity sway angle, and dynamic center-of-gravity velocity characteristics. The center-of-gravity position characteristics include center-of-gravity offset, and the dynamic center-of-gravity velocity characteristics include dynamic center-of-gravity velocity amplitude. This makes the fused data features richer, the fusion results more accurate, and improves the accuracy of gait monitoring results. Furthermore, based on the acquired pressure feature set and center of gravity dynamic balance feature set, the center of gravity and pressure are jointly fused to obtain comprehensive gait data. This comprehensive gait data includes: a balance index, which is the core fusion data, obtained by multiplying the center of gravity offset and the left-right pressure ratio of the foot, and used to comprehensively reflect the gait balance state; a gait stability coefficient, which reflects gait stability and is obtained by combining total pressure and center of gravity sway angle; a sway index, which comprehensively reflects static balance ability and is obtained based on the center of gravity sway angle and center of gravity offset; and a dynamic stability coefficient, which is used to evaluate dynamic balance during walking and is obtained based on center of gravity offset and dynamic center of gravity velocity amplitude. This diverse comprehensive gait data makes the gait monitoring and evaluation more accurate. Based on a preset sampling frequency, strict timestamp matching is ensured. Timestamp difference verification is used to ensure that pressure data and center of gravity dynamic data are completely synchronized in time and space, avoiding misaligned fusion. Attached Figure Description

[0019] The embodiments of this application are described in detail below with reference to the accompanying drawings. These drawings, which form part of this application, are used to provide a further understanding of the application. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application. In the drawings: Figure 1 An exemplary flowchart of a gait intelligent monitoring method according to an example embodiment of this application is shown; Figure 2 This illustrates a standardized stance marker for implementing multi-posture positioning in a gait intelligent monitoring system according to an example embodiment of this application; Figures 3a-3b The diagram illustrates posture positioning for different assessment scenarios of sarcopenia assessment and fall risk assessment according to example embodiments of this application. Figure 4 An exemplary block diagram of a gait intelligent monitoring system according to an example embodiment of this application is shown. Detailed Implementation

[0020] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of this application. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0021] In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first" and "second" are used only for description and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more similar features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0022] In the description of this application, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linkage" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection, an electrical connection, or a communication connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0023] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the relative height of the first feature in a certain dimension is higher than that of the second feature. "Below," "below," and "under" the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the relative position of the first feature in a certain dimension is smaller than that of the second feature.

[0024] Different embodiments or examples are provided below to implement different structures of this application. To simplify this application, the components and arrangements of specific examples are described below. Of course, these are merely examples and are not intended to limit this application. Reference numerals may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements described. Furthermore, this application provides examples of various specific processes and materials, but those skilled in the art can apply other processes and / or substitute other materials based on the teachings of this application.

[0025] The following description, with reference to the accompanying drawings, illustrates some preferred embodiments of the present application. It should be noted that the following description is for illustrative purposes only and is not intended to limit the scope of protection of this application.

[0026] Figure 1 This is an exemplary flowchart of a gait intelligent monitoring method according to some embodiments of this application. See also: Figure 1 The intelligent gait monitoring method 100 may include the following steps.

[0027] Step S1: Based on multi-posture positioning, synchronously collect pressure data and dynamic center of gravity data of different areas of the target object's foot.

[0028] In some specific embodiments, the target is the person being evaluated.

[0029] In some specific embodiments, the multi-posture positioning includes the target object selecting a corresponding position based on standardized position markers according to the evaluation requirements, maintaining the selected position to complete a preset action, and the positioning includes: a straight position with the right foot in front of the left foot, a position with the left and right feet side by side, and a position with the right foot in front of the left foot. The standardized position markers are set on the surface of the monitoring pad.

[0030] In some specific examples, standardized station markers feature a high-contrast, non-slip, and wear-resistant raised texture (0.5mm high and 2mm wide), facilitating accurate positioning for those being evaluated through both tactile and visual guidance. See [link to relevant documentation]. Figures 2-3b The monitoring mat is divided into four monitoring areas: A, B, C, and D. The standardized station markings specifically include three categories: Standing with feet side by side: This is posture A+B. The markings consist of two parallel horizontal guide lines (20cm long, 10cm apart) and symmetrical foot positioning areas. The center distance between the two positioning areas matches the shoulder width of the human body (default 30cm, adjustable range 25-35cm). The edges of the positioning areas have arc-shaped fitting textures to fit the contour of the foot. The right foot forward and left foot back stance (also known as posture A+C) is marked by two parallel vertical guide lines (30cm long and 5cm apart) and foot positioning areas. The right foot positioning area is located at the midpoint of the front guide line, and the left foot positioning area is located at the midpoint of the rear guide line. The line connecting the centers of the two positioning areas is parallel to the guide lines and the distance between them is 15cm (adjustable according to height, with an adjustment range of 12-18cm). The right foot is positioned to the right front of the left foot, i.e., posture A+D. Compared to the right foot in front of the left foot in a straight line and the left and right feet side by side, only the position of the right positioning area is interchanged. The guide lines and spacing parameters remain the same to facilitate meeting the posture requirements in different evaluation scenarios.

[0031] For specific examples, see [link to specific examples]. Figures 2-3b When conducting a sarcopenia muscle strength assessment test on the target subject, the left and right feet can be placed side by side (A+B); when conducting a fall prevention assessment test on the target subject, the right foot in front of the left foot in a straight line (A+C) or the right foot in front of the left foot (A+D) can be placed side by side.

[0032] In some specific examples, multi-posture positioning also includes using auxiliary positioning elements for prompting. These elements include infrared distance sensors and prompting components, which may include, but are not limited to, voice prompts and light prompts. For instance, when monitoring three gait patterns, if the person being assessed enters the positioning area and reaches the preset position, a voice prompt will indicate that the positioning is correct; if there are abnormal distance signal values ​​in the four areas, a voice prompt will indicate that the positioning is incorrect and the person should be placed back in the area to be inspected until the positioning is confirmed to be correct.

[0033] This application's embodiment achieves standardized, visual, tactile guidance, and automatic verification of positioning through a standardized positioning marker design. Positioning deviation is controlled within ±1cm, avoiding data deviations caused by non-standard postures and ensuring data comparability between different assessment scenarios and different assessees. Furthermore, the standardized positioning marker design supports adaptive adjustment of height and shoulder width, making it suitable for assessees of different ages and body types. It is also simple to operate, requiring no professional assistance to complete positioning and monitoring.

[0034] In some specific embodiments, the pressure data includes pressure values, pressure distribution patterns, and pressure change rates in different areas of the sole of the foot, which are collected in real time using a flexible thin-film pressure sensing module. The center of gravity dynamic data includes the three-dimensional displacement, acceleration, and angular velocity data of the body's center of gravity, which are captured in real time using a gravity application module.

[0035] In some specific embodiments, both the flexible thin-film pressure sensing module and the gravity sensing module are disposed on the monitoring pad.

[0036] In some specific examples, the flexible thin-film pressure sensing module includes an array of flexible pressure sensors. These sensors are laid on the foot contact area of ​​the monitoring pad, with a sensor density of no less than 10 sensors / cm². This allows for real-time acquisition of pressure values, pressure distribution patterns, and pressure change rates in different areas of the foot, with a resolution of 0.1N. The embodiments of this application utilize flexible pressure sensors, which offer excellent fit and durability, and the monitoring pad can be folded for storage.

[0037] In some specific examples, the gravity sensing module includes an integrated resistance strain gauge sensor (measurement range 1-100KG, sensitivity 3-5KG 0.5-1.0mv / v) and a three-axis gyroscope (measurement range ±2000° / s, accuracy 0.1° / s). The gravity sensing module is installed at the center of the monitoring pad to capture the three-dimensional displacement, acceleration, and angular velocity changes of the body's center of gravity of the person being assessed, and outputs the center of gravity trajectory data in real time.

[0038] In some specific embodiments, the data from the flexible thin film pressure sensing module and the gravity sensing module are synchronously acquired through the STM32 main control chip to ensure that the sampling frequency is ≥100Hz.

[0039] Step S2: Denoise the synchronously acquired pressure data and center of gravity dynamic data.

[0040] In some specific embodiments, a Kalman filter algorithm is used to denoise the pressure data and the dynamic data of the center of gravity. For details, please refer to existing technologies, which will not be elaborated upon here.

[0041] Step S3: Perform multi-dimensional data fusion on the denoised pressure data and center of gravity dynamic data based on gait cycle, spatial distribution, and dynamic characteristics to generate comprehensive gait data including plantar pressure distribution parameters and center of gravity dynamic balance parameters, including: Establish a spatiotemporal correlation between pressure data and center of gravity dynamic data, and based on the needs of gait analysis, integrate the scattered pressure data and center of gravity dynamic data into a pressure feature set and a center of gravity dynamic balance feature set with gait analysis value; the pressure feature set includes total plantar pressure and the ratio of left to right plantar pressure, and the center of gravity dynamic balance feature set includes center of gravity position features, center of gravity sway angle, and dynamic center of gravity velocity features; the center of gravity position features include center of gravity offset, and the dynamic center of gravity velocity features include dynamic center of gravity velocity amplitude. Based on the acquired pressure feature set and center of gravity dynamic balance feature set, the center of gravity and pressure are jointly fused to obtain comprehensive gait data, which includes: Balance Index: This is the core fusion data, obtained by multiplying the center of gravity offset and the left-right pressure ratio of the foot, and is used to comprehensively reflect the gait balance status. Gait stability coefficient, which reflects gait stability, is obtained by combining total pressure and center of gravity sway angle; Sway Index: A comprehensive reflection of static balance ability, obtained based on the sway angle and center of gravity offset; Dynamic stability coefficient: used to evaluate dynamic balance during walking, based on center of gravity offset and dynamic center of gravity velocity amplitude.

[0042] Specifically, total pressure reflects the total force on the sole of the foot during the gait cycle; The left-right pressure ratio reflects the left-right distribution of the center of gravity and is directly related to the left-right shift of the center of gravity. When the left-right pressure ratio is approximately 1, the center of gravity is in the center. The greater the deviation from 1, the more tilted the center of gravity is, that is, the more serious the left-right imbalance is. Regarding the balance index, the greater the center of gravity shift and the more uneven the pressure distribution (i.e., the greater the deviation of the ratio of left and right pressure on the sole of the foot from 1), the higher the balance index and the more unstable the gait. Regarding the gait stability coefficient, a larger value indicates a more stable gait. Regarding the sway index, an index >5 indicates poor balance ability; Regarding the dynamic stability coefficient, a coefficient <0.5 indicates gait instability.

[0043] In some specific embodiments, a spatiotemporal correlation is established between pressure data and center of gravity dynamic data, and based on the needs of gait analysis, the dispersed pressure data and center of gravity dynamic data are integrated into a pressure feature set and a center of gravity dynamic balance feature set with gait analysis value, including: Step S31, Data Alignment: Based on the preset sampling frequency, ensure strict matching of timestamps, and verify the timestamp difference to ensure that the pressure data and center of gravity dynamic data are completely synchronized in time and space, avoiding misalignment and fusion; preferably, the sampling frequency is ≥100Hz.

[0044] In some specific embodiments, the sampling frequency is ensured to be ≥100Hz by using an STM32 main control chip.

[0045] Furthermore, data alignment includes time synchronization verification, which is achieved by setting a timestamp difference verification error threshold. Specifically, the prerequisite for data fusion is that the two types of data are strictly aligned in time. The code uses timestamp difference verification (allowing an error of 10ms). Data exceeding the error range is marked as invalid to avoid errors caused by misaligned fusion.

[0046] Step S32, Feature Extraction: Based on the aligned data, extract pressure feature sets and center of gravity dynamic balance feature sets with gait analysis value from the denoised pressure data and center of gravity dynamic data.

[0047] In some specific embodiments, the pressure feature set also includes heel / forefoot ratio and peak pressure. The heel / forefoot ratio is used to distinguish gait phases, with a high heel ratio when the heel strikes the ground and a high forefoot ratio when the foot leaves the ground. Peak pressure identifies abnormal stress points, such as plantar fasciitis, hallux valgus, and other problems.

[0048] In some specific embodiments, the dynamic balance feature set of the center of gravity further includes dynamic center of gravity acceleration, and the center of gravity position feature further includes measured coordinate values ​​of the center of gravity and the proportion of center of gravity offset. The proportion of center of gravity offset, taking the proportion of center of gravity offset along the Y-axis as an example, quantifies the degree of left-right balance; a proportion > 0.8 indicates severe left-right imbalance.

[0049] Furthermore, the dynamic balance feature set of the center of gravity also includes dynamic center of gravity angular velocity, and the comprehensive gait data also includes: Balance margin: It is characterized by the anti-tipping ability of the plantar support surface, which is obtained based on the width of the plantar support surface and the dynamic center of gravity angular velocity.

[0050] In some specific embodiments, before performing joint fusion of center of gravity and pressure based on the acquired pressure feature set and center of gravity dynamic balance feature set to obtain comprehensive gait data, the following steps are also included: Step S30: Construct a joint state vector containing the pressure feature set and the center of gravity dynamic equilibrium feature set.

[0051] In some specific examples, the joint state vector includes total pressure, left-right pressure ratio, center of gravity X coordinate, and center of gravity Y coordinate, as shown in the following formula: Z = {Ptotal, Plr, comx, comy} Where Z represents the joint state vector; Ptotal represents the total pressure, reflecting the total force on the sole of the foot during the gait cycle; Plr represents the left-right pressure ratio, reflecting the left-right distribution of the center of gravity, which is directly related to the left-right shift of the center of gravity. When the left-right pressure ratio is ≈1, the center of gravity is in the center. The larger the deviation from 1, the more tilted the center of gravity is, that is, the more serious the left-right imbalance is; comx represents the X coordinate of the center of gravity, and comy represents the Y coordinate of the center of gravity.

[0052] Furthermore, the parameters specifically included in the pressure feature set and the center of gravity dynamic equilibrium feature set in the joint state vector can be found in the above embodiments, and will not be listed one by one here.

[0053] Step S4: Transmit the acquired comprehensive gait data to the evaluation terminal.

[0054] In some specific embodiments, the acquired comprehensive gait data is transmitted to the evaluation terminal via a data transmission module. In this embodiment, Bluetooth 5.0 + USB dual interfaces are used to support real-time data transmission and local storage.

[0055] Step S5: Based on the evaluation terminal selection and multi-posture positioning adaptation evaluation model, the comprehensive gait data is evaluated and identified through the evaluation model to obtain gait recognition results.

[0056] In some specific embodiments, the accompanying assessment terminal includes data visualization software. The software selects an assessment model based on the positioning corresponding to the specific assessment type. The assessment model generates an assessment report based on a preset algorithm; for example, a gait-pressure integral model is used for sarcopenia assessment, and a center of gravity shift coefficient model is used for fall prevention assessment. The assessment report is used for sarcopenia or fall prevention risk assessment.

[0057] Furthermore, it also includes a power supply module with a built-in lithium battery, providing a battery life of ≥8 hours and supporting fast charging.

[0058] In summary, the embodiments of this application are based on multi-posture standardized positioning, which is suitable for assessment in different assessment scenarios such as sarcopenia assessment and fall prevention risk assessment; Specifically, the hardware labeling design of the three types of stations enables visualization, tactile guidance, and automatic verification of the positioning. The positioning deviation is controlled within ±1cm, avoiding data deviation caused by non-standard posture and ensuring data comparability between different assessment scenarios and different assessed persons. In addition, the standardized station supports adaptive adjustment of height and shoulder width, which is suitable for assessed persons of different ages and body types. It is also simple to operate and can be completed without the assistance of professional personnel. In addition, gait monitoring is performed by integrating pressure data and gravity dynamic data to improve monitoring accuracy. Furthermore, the denoised pressure data and center of gravity dynamic data are fused in multiple dimensions based on gait cycle, spatial distribution, and dynamic characteristics to generate comprehensive gait data that includes plantar pressure distribution parameters and center of gravity dynamic balance parameters. This makes the fused data features richer and the fusion results more accurate, thus improving the accuracy of gait monitoring results. In addition, the comprehensive gait data includes the balance index, which is the core fusion data, obtained by multiplying the center of gravity offset and the left-right pressure ratio of the foot, and is used to comprehensively reflect the gait balance state; the gait stability coefficient, which reflects gait stability, obtained by combining total pressure and center of gravity sway angle; the sway index, obtained based on the center of gravity sway angle and center of gravity offset; and the dynamic stability coefficient, obtained based on the center of gravity offset and dynamic center of gravity velocity amplitude. The diverse comprehensive gait data can make the gait monitoring and evaluation effect more accurate. Based on a preset sampling frequency, strict timestamp matching is ensured. Timestamp difference verification is used to ensure that pressure data and center of gravity dynamic data are completely synchronized in time and space, avoiding misaligned fusion. By integrating flexible film pressure sensing and gravity sensing, refined data on plantar pressure and dynamic balance information of the center of gravity are obtained. Compared with a single sensing solution, the measurement error of gait parameters is reduced to ±3%, and the accuracy of center balance assessment is improved by more than 40%, thus improving monitoring accuracy. Furthermore, the flexible thin-film sensor has excellent adhesion and durability, and the monitoring pad can be folded for storage.

[0059] This application provides a gait intelligent monitoring system 200 in some embodiments to implement the gait intelligent monitoring method of the above embodiments. The gait intelligent monitoring system 200 of this application includes: The data acquisition module 210 is configured to simultaneously acquire pressure data and center of gravity dynamic data of different areas of the sole of the target object based on multi-posture positioning; preferably, the data acquisition module includes a flexible film pressure sensing module, a gravity sensing module, and a monitoring pad with standardized station markings, wherein the flexible film pressure sensing module and the gravity sensing module are both disposed on the monitoring pad. The data processing module 220 is configured to perform noise reduction processing on the synchronously acquired pressure data and center of gravity dynamic data. Preferably, the Kalman filter algorithm is used to perform noise reduction processing on the pressure data and center of gravity dynamic data. The multi-dimensional data fusion module 230 is configured to perform multi-dimensional data fusion on the denoised pressure data and center of gravity dynamic data based on gait cycle, spatial distribution, and dynamic features, generating comprehensive gait data including plantar pressure distribution parameters and center of gravity dynamic balance parameters, including: Establish a spatiotemporal correlation between pressure data and center of gravity dynamic data, and based on the needs of gait analysis, integrate the scattered pressure data and center of gravity dynamic data into a pressure feature set and a center of gravity dynamic balance feature set with gait analysis value; the pressure feature set includes total plantar pressure and the ratio of left to right plantar pressure, and the center of gravity dynamic balance feature set includes center of gravity position features, center of gravity sway angle, and dynamic center of gravity velocity features; the center of gravity position features include center of gravity offset, and the dynamic center of gravity velocity features include dynamic center of gravity velocity amplitude. Based on the acquired pressure feature set and center of gravity dynamic balance feature set, the center of gravity and pressure are jointly fused to obtain comprehensive gait data, which includes: Balance Index: This is the core fusion data, obtained by multiplying the center of gravity offset and the left-right pressure ratio of the foot, and is used to comprehensively reflect the gait balance status. Gait stability coefficient, which reflects gait stability, is obtained by combining total pressure and center of gravity sway angle; Sway Index: A comprehensive reflection of static balance ability, obtained based on the sway angle and center of gravity offset; Dynamic stability coefficient: used to evaluate dynamic balance during walking, based on center of gravity offset and dynamic center of gravity velocity amplitude; The transmission module 240 is configured to transmit the acquired integrated gait data to the evaluation terminal; The evaluation and recognition module 250 is configured to select an evaluation model based on the evaluation terminal and adapt it to multiple postures, and to evaluate and recognize the comprehensive gait data through the evaluation model to obtain gait recognition results.

[0060] In the embodiments of this application, the gait intelligent monitoring system may selectively incorporate features of the gait intelligent monitoring method, or vice versa.

[0061] In summary, the embodiments of this application are based on multi-posture standardized positioning, which is suitable for assessment in different assessment scenarios such as sarcopenia assessment and fall prevention risk assessment; Specifically, the hardware labeling design of the three types of stations enables visualization, tactile guidance, and automatic verification of the positioning. The positioning deviation is controlled within ±1cm, avoiding data deviation caused by non-standard posture and ensuring data comparability between different assessment scenarios and different assessed persons. In addition, the standardized station supports adaptive adjustment of height and shoulder width, which is suitable for assessed persons of different ages and body types. It is also simple to operate and can be completed without the assistance of professional personnel. In addition, gait monitoring is performed by integrating pressure data and gravity dynamic data to improve monitoring accuracy. Furthermore, the denoised pressure data and center of gravity dynamic data are fused in multiple dimensions based on gait cycle, spatial distribution, and dynamic characteristics to generate comprehensive gait data that includes plantar pressure distribution parameters and center of gravity dynamic balance parameters. This makes the fused data features richer and the fusion results more accurate, thus improving the accuracy of gait monitoring results. In addition, the comprehensive gait data includes the balance index, which is the core fusion data, obtained by multiplying the center of gravity offset and the left-right pressure ratio of the foot, and is used to comprehensively reflect the gait balance state; the gait stability coefficient, which reflects gait stability, obtained by combining total pressure and center of gravity sway angle; the sway index, obtained based on the center of gravity sway angle and center of gravity offset; and the dynamic stability coefficient, obtained based on the center of gravity offset and dynamic center of gravity velocity amplitude. The diverse comprehensive gait data can make the gait monitoring and evaluation effect more accurate. Based on a preset sampling frequency, strict timestamp matching is ensured. Timestamp difference verification is used to ensure that pressure data and center of gravity dynamic data are completely synchronized in time and space, avoiding misaligned fusion. By integrating flexible film pressure sensing and gravity sensing, refined data on plantar pressure and dynamic balance information of the center of gravity are obtained. Compared with a single sensing solution, the measurement error of gait parameters is reduced to ±3%, and the accuracy of center balance assessment is improved by more than 40%, thus improving monitoring accuracy. Furthermore, the flexible thin-film sensor has excellent adhesion and durability, and the monitoring pad can be folded for storage.

[0062] In some embodiments, this application also provides an electronic device that may include a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, can implement the method described in any of the above embodiments.

[0063] In some embodiments, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any one of the above embodiments. The computer program includes various program modules / units constituting the apparatus according to embodiments of this application. When the computer program, composed of these program modules / units, is executed, it can perform functions corresponding to the steps of the methods described in the above embodiments. The computer program can also run on electronic devices as described in embodiments of this application.

[0064] Although not shown, some embodiments also provide a program product including a computer program, wherein the computer program, when executed by a processor, implements the method described in any of the above embodiments.

[0065] The basic concepts have been described herein. It is obvious that the detailed disclosure above is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, various modifications, improvements, and corrections may be made to this application by those skilled in the art. Such modifications, improvements, and corrections are suggested in this application and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0066] Furthermore, this application uses specific terms to describe its embodiments. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this application do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0067] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, aspects of this application can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “unit,” “component,” or “system.” Furthermore, aspects of this application may manifest as a computer product located on one or more computer-readable media, the product including computer-readable program code.

[0068] Computer storage media may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and suitable combinations thereof. Computer storage media can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0069] The computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby, and Groovy, or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0070] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although the foregoing disclosure has discussed some currently considered useful embodiments of the invention through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely through software solutions, such as installing the described system on existing servers or mobile devices.

[0071] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this disclosure method does not imply that the subject matter of the application requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of the single embodiments disclosed above.

[0072] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of scope in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0073] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this application, the entire contents of that material are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this application, as well as documents that limit the broadest scope of the claims in this application (currently or subsequently appended to this application). It should be noted that if there is any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials of this application and the content of this application, the descriptions, definitions, and / or terminology used in this application shall prevail.

[0074] Finally, it should be noted that the above descriptions are merely exemplary embodiments of this application and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A gait intelligent monitoring method, characterized in that, include: Based on multi-posture positioning, pressure data and dynamic center of gravity data of different areas of the target object's foot are collected simultaneously. The pressure data and center of gravity dynamic data collected synchronously are denoised using a Kalman filter algorithm. The denoised pressure data and center-of-gravity dynamic data are fused in multiple dimensions based on gait cycle, spatial distribution, and dynamic characteristics to generate comprehensive gait data that includes plantar pressure distribution parameters and center-of-gravity dynamic balance parameters, including: Establish a spatiotemporal correlation between pressure data and center of gravity dynamic data, and based on the needs of gait analysis, integrate the scattered pressure data and center of gravity dynamic data into a pressure feature set and a center of gravity dynamic balance feature set with gait analysis value; the pressure feature set includes total plantar pressure and the ratio of left to right plantar pressure, and the center of gravity dynamic balance feature set includes center of gravity position features, center of gravity sway angle, and dynamic center of gravity velocity features; the center of gravity position features include center of gravity offset, and the dynamic center of gravity velocity features include dynamic center of gravity velocity amplitude. Based on the acquired pressure feature set and center of gravity dynamic balance feature set, the center of gravity and pressure are jointly fused to obtain comprehensive gait data, which includes: Balance Index: This is the core fusion data, obtained by multiplying the center of gravity offset and the left-right pressure ratio of the foot, and is used to comprehensively reflect the gait balance status. Gait stability coefficient, which reflects gait stability, is obtained by combining total pressure and center of gravity sway angle; Sway Index: A comprehensive reflection of static balance ability, obtained based on the sway angle and center of gravity offset; Dynamic stability coefficient: used to evaluate dynamic balance during walking, based on center of gravity offset and dynamic center of gravity velocity amplitude; The acquired comprehensive gait data is transmitted to the evaluation terminal; Based on the evaluation model for terminal selection and multi-posture positioning adaptation, the comprehensive gait data is evaluated and identified through the evaluation model to obtain gait recognition results.

2. The gait intelligent monitoring method according to claim 1, wherein the pressure feature set further includes heel / forefoot ratio and peak pressure, the center of gravity dynamic balance feature set further includes dynamic center of gravity acceleration, and the center of gravity position feature further includes measured center of gravity coordinate value and center of gravity offset ratio.

3. The intelligent gait monitoring method according to claim 1, wherein the dynamic balance feature set of the center of gravity further includes dynamic center of gravity angular velocity, and the comprehensive gait data further includes: Balance margin: It is characterized by the anti-tipping ability of the plantar support surface, which is obtained based on the width of the plantar support surface and the dynamic center of gravity angular velocity.

4. The gait intelligent monitoring method according to any one of claims 1-3, establishing a spatiotemporal correlation between pressure data and center of gravity dynamic data, and integrating the dispersed pressure data and center of gravity dynamic data into a pressure feature set and a center of gravity dynamic balance feature set with gait analysis value based on the needs of gait analysis, including: Data alignment: Based on a preset sampling frequency, strict timestamp matching is ensured. Timestamp difference verification is used to ensure that pressure data and center of gravity dynamic data are completely synchronized in time and space, avoiding misalignment and fusion; the sampling frequency is ≥100Hz. Feature extraction: Based on aligned data, extract pressure feature sets and center of gravity dynamic balance feature sets with gait analysis value from denoised pressure data and center of gravity dynamic data.

5. The gait intelligent monitoring method according to claim 1, wherein the multi-posture positioning includes the target object selecting a corresponding position based on standardized position markers and evaluation requirements, and maintaining the selected position to complete a preset action, wherein the positioning includes: The standardized position markings are set on the surface of the monitoring pad, including the following positions: standing with the right foot in front of the left foot, standing with the left and right feet side by side, and standing with the right foot in front of the left foot.

6. The intelligent gait monitoring method according to claim 1, wherein the pressure data includes pressure values, pressure distribution patterns and pressure change rates of different areas of the sole of the foot collected in real time using a flexible thin-film pressure sensing module; The center of gravity dynamic data includes the three-dimensional displacement, acceleration, and angular velocity data of the body's center of gravity, which are captured in real time using a gravity sensing module.

7. A gait intelligent monitoring system, characterized in that, include: The data acquisition module is configured to simultaneously collect pressure data and dynamic center of gravity data of different areas of the sole of the target object based on multi-posture positioning. The data acquisition module includes a flexible film pressure sensing module, a gravity sensing module, and a monitoring pad with standardized station markings. The flexible film pressure sensing module and the gravity sensing module are both installed on the monitoring pad. The data processing module is configured to denoise the synchronously acquired pressure data and center of gravity dynamic data, and uses the Kalman filter algorithm to denoise the pressure data and center of gravity dynamic data. The multi-dimensional data fusion module is configured to perform multi-dimensional data fusion on denoised pressure data and center of gravity dynamic data based on gait cycle, spatial distribution, and dynamic characteristics, generating comprehensive gait data including plantar pressure distribution parameters and center of gravity dynamic balance parameters, including: Establish a spatiotemporal correlation between pressure data and center of gravity dynamic data, and based on the needs of gait analysis, integrate the scattered pressure data and center of gravity dynamic data into a pressure feature set and a center of gravity dynamic balance feature set with gait analysis value; the pressure feature set includes total plantar pressure and the ratio of left to right plantar pressure, and the center of gravity dynamic balance feature set includes center of gravity position features, center of gravity sway angle, and dynamic center of gravity velocity features; the center of gravity position features include center of gravity offset, and the dynamic center of gravity velocity features include dynamic center of gravity velocity amplitude. Based on the acquired pressure feature set and center of gravity dynamic balance feature set, the center of gravity and pressure are jointly fused to obtain comprehensive gait data, which includes: Balance Index: This is the core fusion data, obtained by multiplying the center of gravity offset and the left-right pressure ratio of the foot, and is used to comprehensively reflect the gait balance status. Gait stability coefficient, which reflects gait stability, is obtained by combining total pressure and center of gravity sway angle; Sway Index: A comprehensive reflection of static balance ability, obtained based on the sway angle and center of gravity offset; Dynamic stability coefficient: used to evaluate dynamic balance during walking, based on center of gravity offset and dynamic center of gravity velocity amplitude; A transmission module configured to transmit the acquired integrated gait data to an evaluation terminal; The evaluation and recognition module is configured to select an evaluation model based on the evaluation terminal and adapt it to multiple postures, and to evaluate and recognize the comprehensive gait data through the evaluation model to obtain gait recognition results.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-6.

10. A program product, characterized in that, Includes a computer program, wherein the computer program, when executed by a processor, implements the method as described in any one of claims 1-6.

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