Abnormal gait correction method and device based on foot electrostatic gait signal

Abstract

The invention provides an abnormal gait correction method and device based on a foot electrostatic gait signal, and relates to the technical field of medical instruments. And performing preprocessing and feature point extraction on the signal by using variational mode decomposition and a self-adaptive dynamic threshold algorithm so as to establish gait fingerprint features of the user as a reference. Then, the gait is monitored in real time through multi-dimensional analysis, and once an abnormal gait is detected, a correction signal is generated according to gait fingerprint features; the device is composed of an electrostatic induction electrode, a signal processing module, a monitoring analysis module and a correction output module. The non-contact electrostatic induction method is adopted, discomfort caused by the fact that the sensor makes direct contact with the human body is avoided, meanwhile, the accuracy and timeliness of abnormal gait recognition are ensured by combining multi-dimensional analysis, the method is suitable for gait monitoring and correction in all weathers and all scenes, and the walking safety of a user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and in particular to a method and device for correcting abnormal gait based on electrostatic gait signals of feet. Background Art

[0002] As the population ages, more and more elderly people need to use crutches to assist in walking. For elderly people with poor balance, poor eyesight, osteoporosis, joint dysfunction, and stroke, crutches have become an indispensable aid in their daily lives. However, these elderly people often experience unstable gait and loss of normal gait rhythm when walking with crutches, which not only affects their normal walking but also increases the risk of falling.

[0003] At present, common gait monitoring methods mainly include visual monitoring, wearable sensor monitoring, etc. Although the visual monitoring method can observe gait changes intuitively, it is easily affected by environmental factors such as light and occlusion, and requires a special monitoring site, making it difficult to achieve monitoring anytime and anywhere. Although the wearable sensor monitoring method can achieve continuous monitoring, wearing the sensor will cause discomfort to the user, affecting their natural gait, and the equipment cost is high, which is not conducive to large-scale promotion and application.

[0004] When walking, the human body will carry static electricity through various physical processes, and a corresponding electric field will be generated in the surrounding space. When the human body moves, the electric field will change accordingly. By detecting the changes in the human body's electric field when walking, real-time gait information can be obtained. However, there is no technical solution that uses the principle of electrostatic induction to monitor and correct gait. Summary of the invention

[0005] The purpose of the present invention is to provide a method and device for correcting abnormal gait based on electrostatic gait signals of the feet, which is easy to use, low-cost, can monitor gait abnormalities in real time and provide correction prompts for abnormal gait, and can overcome the problems of strong environmental dependence, inconvenient monitoring, high cost, etc. in the prior art, and can achieve accurate monitoring and timely correction of the user's gait, thereby improving their walking safety.

[0006] To achieve the above object, the present invention is implemented through the following technical solutions: A method for correcting abnormal gait based on electrostatic gait signals of the foot comprises the following steps: S202: Collecting foot static electricity signals generated when the user walks; S204: performing signal preprocessing on the foot electrostatic signal, extracting gait features through multi-dimensional analysis, and establishing a user gait fingerprint feature library; Step S204 specifically includes: Using variational mode decomposition method to filter the foot electrostatic signal; Adopting the peak detection algorithm with adaptive dynamic threshold to extract gait feature points; Extracting gait time parameters from gait feature points, wherein the gait time parameters include step frequency, gait cycle, support period and swing period; Generate user gait fingerprint features by combining multi-dimensional analysis content, wherein the multi-dimensional analysis content includes gait variability analysis, gait symmetry analysis, gait stability analysis and gait similarity analysis; The user's gait fingerprint feature is stored as a reference feature to establish a user's gait fingerprint feature library; S206: During the real-time gait monitoring process, the foot electrostatic signal collected in real time is compared based on the gait fingerprint feature library to determine whether there is an abnormal gait; S208: When an abnormal gait is detected, a gait correction signal is generated based on the fingerprint feature of the abnormal gait.

[0007] Furthermore: the specific steps of the multi-dimensional analysis include: Gait variability analysis, performing gait time parameter analysis in the time domain and calculating the coefficient of variation of the gait time parameter; Gait symmetry analysis, using a dynamic time planning algorithm in the frequency domain to calculate the similarity of bipedal gait signals; Gait stability analysis: calculate the energy distribution ratio of gait signals in each frequency band in the energy domain, and calculate the gait stability index based on the energy distribution; Gait similarity analysis uses the detrended fluctuation analysis method to process the gait signal in sections, calculates the fluctuation function after removing the trend, and evaluates the autocorrelation degree of the gait signal based on the fluctuation function.

[0008] Furthermore: if during the real-time gait monitoring process, the coefficient of variation of the foot electrostatic signal collected in real time exceeds the corresponding coefficient of variation in the user's gait fingerprint feature library by 30%, it is determined that the gait is abnormal.

[0009] If, during real-time gait monitoring, the similarity coefficient of the foot electrostatic signal collected in real time exceeds the corresponding similarity coefficient in the user's gait fingerprint feature library by 30%, the gait is judged to be abnormal.

[0010] If, during real-time gait monitoring, the stability index of the foot electrostatic signal collected in real time exceeds the corresponding stability index in the user's gait fingerprint feature library by 30%, the gait is judged to be abnormal.

[0011] If, during real-time gait monitoring, the autocorrelation coefficient of the foot electrostatic signal collected in real time exceeds the corresponding autocorrelation coefficient in the user's gait fingerprint feature library by 30%, it is determined that the gait is abnormal.

[0012] Further: the calculating the energy distribution ratio of the gait signal in each frequency band in the energy domain includes: The empirical mode decomposition method is used to decompose the gait signal into multiple intrinsic mode function components; Calculate the energy distribution of each eigenmode function component; The gait stability index was calculated based on the energy distribution.

[0013] The present invention also provides an abnormal gait correction device based on foot electrostatic gait signals, comprising: Electrostatic induction electrodes are used to collect static electricity signals from the user's feet when walking; A signal processing module, used to perform signal preprocessing on the foot electrostatic signal, extract gait features through multi-dimensional analysis, and establish a user gait fingerprint feature library; The signal processing module comprises: A signal preprocessing unit, used for filtering the foot electrostatic signal by using a variational mode decomposition method; A feature extraction unit, used for extracting gait feature points by using a peak detection algorithm with an adaptive dynamic threshold; A fingerprint generating unit, used for generating a user gait fingerprint feature based on the gait feature points; a variability calculation unit, used for performing gait time parameter analysis in the time domain and calculating the coefficient of variation of the gait time parameter; A symmetry calculation unit, used to calculate the similarity coefficient of the bipedal gait signal using a dynamic time planning algorithm in the frequency domain; A stability calculation unit, used to calculate the energy distribution ratio of the gait signal in each frequency band in the energy domain, and calculate the gait stability index based on the energy distribution; A similarity calculation unit, used for segmenting the gait signal using a detrended fluctuation analysis method, calculating a fluctuation function after removing the trend, and evaluating the autocorrelation coefficient of the gait signal based on the fluctuation function; The monitoring and analysis module compares the foot electrostatic signals collected in real time with the gait fingerprint feature library during real-time gait monitoring to determine whether there is an abnormal gait; The correction output module is used to generate a gait correction signal based on the gait fingerprint feature when an abnormal gait is detected.

[0014] Further: the stability calculation unit includes: A signal decomposition subunit, used for decomposing the gait signal into a plurality of intrinsic mode function components by using an empirical mode decomposition method; An energy calculation subunit, used to calculate the energy distribution of each intrinsic mode function component; The stability calculation subunit is used to calculate the gait stability index based on the energy distribution.

[0015] The present invention also provides an intelligent guiding crutch for abnormal gait correction based on foot electrostatic gait signals, comprising: The crutch body; An abnormal gait correction device is arranged on the crutch body, and the abnormal gait correction device is the abnormal gait correction device mentioned above.

[0016] A prompting device is arranged on the crutch body, and the prompting device is connected to the correction output module of the abnormal gait correction device.

[0017] Furthermore: the prompting device includes a voice prompter and / or a vibration prompter.

[0018] Further: Also including: A power module, which is disposed in the crutch body and is used to supply power to the abnormal gait correction device and the prompt device; A switch button is arranged on the crutch body and is electrically connected to the power module.

[0019] The present invention also provides an abnormal gait correction intelligent guidance method based on the abnormal gait correction intelligent guidance crutch, comprising: Collect the user's initial gait information and establish the user's gait fingerprint characteristics based on the user's gait information in a normal walking state; Real-time gait monitoring, comparing and analyzing the user's gait information collected in real time with the user's gait fingerprint features; Determine abnormal gait: if the deviation between any gait parameter in the real-time collected user gait information and the corresponding parameter in the user gait fingerprint feature exceeds a predetermined threshold, it is determined to be an abnormal gait; Output correction prompts. According to the type of abnormal gait, the abnormal gait correction device generates corresponding gait correction signals and transmits them to the prompt device, which outputs correction prompt information.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention performs gait monitoring based on foot electrostatic signals and adopts a non-contact electrostatic induction method, thereby avoiding the discomfort caused by direct contact of the sensor with the human body. The user can maintain a natural gait state, thereby obtaining more real and reliable gait information.

[0021] 2. The present invention comprehensively evaluates the gait state through multi-dimensional analysis methods such as gait variability analysis, symmetry analysis, stability analysis and similarity analysis, and can identify abnormal gait in a timely and accurate manner, avoiding misjudgment that may be caused by single-dimensional analysis.

[0022] 3. The present invention integrates the abnormal gait correction device into the crutch body, has a simple and compact structure, low cost, and is not affected by environmental factors such as light. It can realize all-weather and all-scenario gait monitoring and correction prompts, and has strong practicality.

[0023] 4. The present invention establishes a gait fingerprint according to the user's personalized gait characteristics, and performs abnormal gait monitoring and correction based on this, thereby improving the accuracy of gait monitoring and the pertinence of the correction effect, and can effectively help the user restore a normal gait and improve walking safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of an application of an intelligent guiding crutch for abnormal gait correction based on electrostatic gait signals of the foot; Figure 2 A schematic diagram of a flow chart of an abnormal gait correction method based on foot electrostatic gait signals; Figure 3 A schematic diagram of a framework of an abnormal gait correction device based on foot electrostatic gait signals; Figure 4 Schematic diagram of the specific framework for abnormal gait correction based on foot electrostatic gait signals. DETAILED DESCRIPTION

[0025] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0027] The abnormal gait correction method and device based on foot electrostatic gait signals provided by the present invention are described in detail below with reference to the accompanying drawings.

[0028] like Figure 1 As shown, the present invention provides an abnormal gait correction intelligent guiding crutch based on foot electrostatic gait signals, including a crutch body 103; an abnormal gait correction device 101 arranged on the crutch body 103, including an electrostatic induction electrode 301, a signal processing module 302, a monitoring and analysis module 303 and a correction output module 304; and a prompt device 102 arranged on the crutch body 103. Among them, the electrostatic induction electrode 301 is used to collect the foot electrostatic signal generated when the user walks, the signal processing module 302 is used to pre-process the electrostatic signal and establish the user's gait fingerprint feature, the monitoring and analysis module 303 is used to perform multi-dimensional gait analysis and judge abnormal gait, the correction output module 304 is used to generate a gait correction signal, and the prompt device 102 is used to output correction prompt information.

[0029] like Figure 2 As shown, the specific implementation process of the abnormal gait correction method based on the electrostatic gait signal of the foot includes: first, the electrostatic signal of the foot generated when the user walks is collected through the electrostatic induction electrode 301. Due to the interference of the power system and electrical equipment in the environment, the collected signal is inevitably mixed with 50Hz power frequency interference and its integer multiple power frequency harmonic noise. In order to improve the signal quality, the variational mode decomposition method is used to filter and preprocess the signal. This method determines the frequency center and bandwidth of each component by iteratively searching for the optimal solution of the variational model, and can adaptively realize the frequency domain decomposition of the signal and the effective separation of each component, thereby effectively removing the interference noise in the signal.

[0030] Next, the peak detection algorithm of adaptive dynamic threshold is used to extract gait feature points. Since the integral signal extreme value points of the electrostatic gait signal fluctuate greatly, the detection of fixed threshold is prone to misjudgment and missed detection. The adaptive dynamic threshold algorithm first determines the initial threshold, that is, the average value of the maximum value of the electrostatic gait signal data in the first few seconds, and then dynamically updates the threshold according to the amplitude of the adjacent extreme value points during the signal detection process, so as to accurately obtain gait feature points such as the moment of foot contact and the moment of foot leaving the ground. Based on these feature points, time parameters such as step frequency, gait cycle, support period and swing period are extracted, and the user gait fingerprint features are generated. Step frequency refers to the frequency of footsteps, that is, the number of times the two legs alternate in a unit time when walking. The cycle refers to the process from the heel of one lower limb touching the ground to the next landing, that is, a complete gait cycle. In this process, the lower limb goes through the two stages of support period and swing period. The cycle is the basic unit of gait analysis. By observing and measuring various parameters in the gait cycle, the quality and characteristics of the gait can be evaluated. The ratio of the swing phase to the support phase refers to the ratio of the time occupied by the swing phase to the time occupied by the support phase in a gait cycle. This ratio reflects the relative time distribution of the lower limbs swinging in the air and contacting the ground during gait, which is of great significance for evaluating the efficiency and stability of gait. Symmetry refers to the similarity or consistency of the movement patterns and characteristics of the lower limbs on both sides during the gait cycle, including symmetry in many aspects such as step length, step frequency, joint angles, and muscle activity. The symmetry of gait is of great significance for maintaining the balance and stability of the body. When the gait is asymmetrical, the body may be abnormal. Stability refers to the ability of the body to maintain balance and prevent falls during walking, including stability in the vertical direction (preventing the body from shaking up and down) and stability in the horizontal direction (preventing the body from swaying left and right or falling). Nonlinearity means that the gait parameters or gait characteristics are not simple linear relationships in time and space, but present complex and irregular change characteristics. Understanding the nonlinear characteristics of gait helps to have a deeper understanding of the generation mechanism and regulation rules of gait.

[0031] In the real-time gait monitoring stage and the early stage of establishing the gait fingerprint library, the collected foot electrostatic signals are analyzed in multiple dimensions. In the time domain, the coefficient of variation of the gait time parameters is calculated, that is, the mean value is divided by the standard deviation, which is used to evaluate the variability of the gait. In the frequency domain, the dynamic time planning algorithm is used to calculate the similarity of the bipedal gait signals. This algorithm evaluates the symmetry of the gait by finding the best time warping function to locally scale the two sequences on the time axis. In the energy domain, the empirical mode decomposition method is used to decompose the signal into multiple intrinsic mode function components, calculate the energy distribution ratio of the signal in each frequency band, and calculate the gait stability index based on the energy distribution. At the same time, the detrended fluctuation analysis method is used to evaluate the gait similarity. This method obtains the intrinsic fluctuation form and autocorrelation characteristics of the sequence by segmenting the time series and removing the trend.

[0032] When an abnormal gait is detected, the correction output module 304 generates a corresponding gait correction signal according to the abnormal type. The prompt device 102 includes a voice prompter and a vibration prompter, which can guide the user to adjust the gait through voice prompts or vibrations. For example, when an abnormal step frequency is detected, it will prompt "the left foot is too fast, please slow down the left foot"; when an abnormal support period is detected, it will prompt "the right foot is lifted too high, please lift the right foot lightly", etc., to help the user gradually restore a normal gait.

[0033] In addition, the intelligent guiding crutch of the present invention further includes a power module disposed in the crutch body 103 for supplying power to each functional module, and a switch button disposed on the crutch body 103 for controlling the opening and closing of the device. The entire device has a simple structure, a compact size, is easy to install and use, and is suitable for use in various scenarios.

[0034] The present invention adopts a non-contact electrostatic induction method to collect gait signals, and combines multi-dimensional analysis technology to realize accurate identification and timely correction of abnormal gait. It not only avoids the discomfort that may be caused by contact sensors in traditional methods, but also overcomes the influence of environmental factors such as light. It can provide convenient and reliable gait monitoring and correction services for special groups such as the elderly who use crutches.

[0035] The above is a brief introduction to the present invention. The present invention will be described in detail below through specific embodiments.

[0036] A method for correcting abnormal gait based on electrostatic gait signals of the foot, such as Figure 2 As shown, the following steps are included: S202: Collecting foot static electricity signals generated when the user walks; S204: performing signal preprocessing on the foot electrostatic signal, extracting gait features through multi-dimensional analysis, and establishing a user gait fingerprint feature library; In some embodiments, the preprocessing of the foot electrostatic signal includes: Using variational mode decomposition method to filter the foot electrostatic signal; The peak detection algorithm with adaptive dynamic threshold is used to extract gait feature points.

[0037] In some embodiments, Figure 4 As shown, the step of establishing the user's gait fingerprint feature includes: Extracting gait time parameters from gait feature points, wherein the gait time parameters include step frequency, gait cycle, support period and swing period; Generate user gait fingerprint features by combining multi-dimensional analysis content, wherein the multi-dimensional analysis content includes gait variability analysis, gait symmetry analysis, gait stability analysis and gait similarity analysis; The user's gait fingerprint feature is stored as a reference feature to establish a user's gait fingerprint feature library.

[0038] In some embodiments, the specific steps of the multi-dimensional analysis include: Gait variability analysis, performing gait time parameter analysis in the time domain and calculating the coefficient of variation of the gait time parameter; Gait symmetry analysis, using a dynamic time planning algorithm in the frequency domain to calculate the similarity coefficient of bipedal gait signals; Gait stability analysis: calculate the energy distribution ratio of gait signals in each frequency band in the energy domain, and calculate the gait stability index based on the energy distribution; The step of calculating the energy distribution ratio of the gait signal in each frequency band in the energy domain includes: The empirical mode decomposition method is used to decompose the gait signal into multiple intrinsic mode function components; Calculate the energy distribution of each eigenmode function component; The gait stability index was calculated based on the energy distribution.

[0039] Gait similarity analysis uses the detrended fluctuation analysis method to process the gait signal in sections, calculates the fluctuation function after removing the trend, and evaluates the autocorrelation coefficient of the gait signal based on the fluctuation function.

[0040] S206: During the real-time gait monitoring process, the foot electrostatic signal collected in real time is compared based on the gait fingerprint feature library to determine whether there is an abnormal gait; In some embodiments, if during the real-time gait monitoring process, the coefficient of variation of the foot electrostatic signal collected in real time exceeds the corresponding coefficient of variation in the user's gait fingerprint feature library by 30%, then it is determined that the gait is abnormal; If, during the real-time gait monitoring process, the similarity coefficient of the foot electrostatic signal collected in real time exceeds the corresponding similarity coefficient in the user's gait fingerprint feature library by 30%, then the gait is judged to be abnormal; If, during the real-time gait monitoring process, the stability index of the foot electrostatic signal collected in real time exceeds the corresponding stability index in the user's gait fingerprint feature library by 30%, the gait is judged to be abnormal; If, during real-time gait monitoring, the autocorrelation coefficient of the foot electrostatic signal collected in real time exceeds the corresponding autocorrelation coefficient in the user's gait fingerprint feature library by 30%, it is determined that the gait is abnormal.

[0041] S208: When an abnormal gait is detected, a gait correction signal is generated based on the gait fingerprint feature.

[0042] like Figure 3 As shown, in other embodiments, the present invention further provides an abnormal gait correction device based on foot electrostatic gait signals, comprising: The electrostatic induction electrode 301 is used to collect the electrostatic signal of the foot generated when the user walks; The signal processing module 302 is used to perform signal preprocessing on the foot electrostatic signal to establish a user gait fingerprint feature; In one embodiment, the signal processing module 302 includes: A signal preprocessing unit, used for filtering the foot electrostatic signal by using a variational mode decomposition method; a feature extraction unit, used for extracting gait feature points by using an adaptive dynamic threshold peak detection algorithm; a fingerprint generation unit, used for generating a user gait fingerprint feature based on the gait feature points; a variability calculation unit, used for performing gait time parameter analysis in the time domain and calculating the coefficient of variation of the gait time parameter; A symmetry calculation unit, used to calculate the similarity coefficient of the bipedal gait signal using a dynamic time planning algorithm in the frequency domain; A stability calculation unit, used to calculate the energy distribution ratio of the gait signal in each frequency band in the energy domain, and calculate the gait stability index based on the energy distribution; The similarity calculation unit is used to process the gait signal in sections by adopting a detrended fluctuation analysis method, calculate the fluctuation function after removing the trend, and evaluate the autocorrelation coefficient of the gait signal based on the fluctuation function.

[0043] The stability calculation unit includes: A signal decomposition subunit, used for decomposing the gait signal into a plurality of intrinsic mode function components by using an empirical mode decomposition method; An energy calculation subunit, used to calculate the energy distribution of each intrinsic mode function component; The stability calculation subunit is used to calculate the gait stability index based on the energy distribution.

[0044] The monitoring and analysis module 303 is used to perform multi-dimensional analysis on the foot electrostatic signal collected in real time based on the gait fingerprint feature to determine whether there is an abnormal gait; The correction output module 304 is used to generate a gait correction signal based on the gait fingerprint feature when an abnormal gait is detected.

[0045] In another embodiment, the present invention further provides an intelligent guiding crutch for abnormal gait correction based on electrostatic gait signals of the foot, comprising: Cane body 103; The abnormal gait correction device 101 is arranged on the crutch body 103 , and the abnormal gait correction device 101 is the abnormal gait correction device 101 mentioned above.

[0046] The prompt device 102 is arranged on the crutch body 103, and the prompt device 102 is connected to the correction output module 304 of the abnormal gait correction device 101. The prompt device 102 includes a voice prompter and a vibration prompter.

[0047] Also includes: A power module, which is disposed in the crutch body 103 and is used to supply power to the abnormal gait correction device 101 and the prompt device 102; A switch button is disposed on the crutch body 103 and is electrically connected to the power module.

[0048] In another embodiment, the present invention further provides an abnormal gait correction intelligent guidance method based on the abnormal gait correction intelligent guidance crutch, comprising: Collect the user's initial gait information and establish the user's gait fingerprint characteristics based on the user's gait information in a normal walking state; Real-time gait monitoring, comparing and analyzing the user's gait information collected in real time with the user's gait fingerprint features; Determine abnormal gait: if the deviation between any gait parameter in the real-time collected user gait information and the corresponding parameter in the user gait fingerprint feature exceeds a predetermined threshold, it is determined to be an abnormal gait; Output correction prompts. According to the type of abnormal gait, the abnormal gait correction device 101 generates a corresponding gait correction signal and transmits it to the prompt device 102. The prompt device 102 outputs correction prompt information.

[0049] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with the technology to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent transformation or modification made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for correcting abnormal gait based on electrostatic gait signals of the foot, characterized in that: The following steps are involved: S202: Collecting foot static electricity signals generated when the user walks; S204: performing signal preprocessing on the foot electrostatic signal, extracting gait features through multi-dimensional analysis, and establishing a user gait fingerprint feature library; Step S204 specifically includes: Using variational mode decomposition method to filter the foot electrostatic signal; Adopting the peak detection algorithm with adaptive dynamic threshold to extract gait feature points; Extracting gait time parameters from gait feature points, wherein the gait time parameters include step frequency, gait cycle, support period and swing period; Generate user gait fingerprint features by combining multi-dimensional analysis content, wherein the multi-dimensional analysis content includes gait variability analysis, gait symmetry analysis, gait stability analysis and gait similarity analysis; The user's gait fingerprint feature is stored as a reference feature to establish a user's gait fingerprint feature library; S206: During the real-time gait monitoring process, the foot electrostatic signal collected in real time is compared based on the gait fingerprint feature library to determine whether there is an abnormal gait; S208: When an abnormal gait is detected, a gait correction signal is generated based on the fingerprint feature of the abnormal gait.

2. The abnormal gait correction method based on foot electrostatic gait signal according to claim 1, characterized in that: The specific steps of the multi-dimensional analysis include: Gait variability analysis, performing gait time parameter analysis in the time domain and calculating the coefficient of variation of the gait time parameter; Gait symmetry analysis, using a dynamic time planning algorithm in the frequency domain to calculate the similarity coefficient of bipedal gait signals; Gait stability analysis: calculate the energy distribution ratio of gait signals in each frequency band in the energy domain, and calculate the gait stability index based on the energy distribution; Gait similarity analysis uses the detrended fluctuation analysis method to process the gait signal in sections, calculates the fluctuation function after removing the trend, and evaluates the autocorrelation coefficient of the gait signal based on the fluctuation function.

3. The abnormal gait correction method based on foot electrostatic gait signal according to claim 2, characterized in that: If, during the real-time gait monitoring process, the coefficient of variation of the foot electrostatic signal collected in real time exceeds the corresponding coefficient of variation in the user's gait fingerprint feature library by 30%, then the gait is judged to be abnormal; If, during the real-time gait monitoring process, the similarity coefficient of the foot electrostatic signal collected in real time exceeds the corresponding similarity coefficient in the user's gait fingerprint feature library by 30%, then the gait is judged to be abnormal; If, during the real-time gait monitoring process, the stability index of the foot electrostatic signal collected in real time exceeds the corresponding stability index in the user's gait fingerprint feature library by 30%, the gait is judged to be abnormal; If, during real-time gait monitoring, the autocorrelation coefficient of the foot electrostatic signal collected in real time exceeds the corresponding autocorrelation coefficient in the user's gait fingerprint feature library by 30%, it is determined that the gait is abnormal.

4. The abnormal gait correction method based on foot electrostatic gait signal according to claim 2, characterized in that: The calculating the energy distribution ratio of the gait signal in each frequency band in the energy domain includes: The empirical mode decomposition method is used to decompose the gait signal into multiple intrinsic mode function components; Calculate the energy distribution of each eigenmode function component; The gait stability index was calculated based on the energy distribution.

5. An abnormal gait correction device based on foot electrostatic gait signals, characterized in that: include: Electrostatic induction electrodes are used to collect static electricity signals from the user's feet when walking; A signal processing module, used to perform signal preprocessing on the foot electrostatic signal, extract gait features through multi-dimensional analysis, and establish a user gait fingerprint feature library; The signal processing module comprises: A signal preprocessing unit, used for filtering the foot electrostatic signal by using a variational mode decomposition method; a feature extraction unit, used for extracting gait feature points by using an adaptive dynamic threshold peak detection algorithm; a fingerprint generation unit, used for generating a user gait fingerprint feature based on the gait feature points; a variability calculation unit, used for performing gait time parameter analysis in the time domain and calculating the coefficient of variation of the gait time parameter; A symmetry calculation unit, used to calculate the similarity coefficient of the bipedal gait signal using a dynamic time planning algorithm in the frequency domain; A stability calculation unit, used to calculate the energy distribution ratio of the gait signal in each frequency band in the energy domain, and calculate the gait stability index based on the energy distribution; A similarity calculation unit, used for segmenting the gait signal using a detrended fluctuation analysis method, calculating a fluctuation function after removing the trend, and evaluating the autocorrelation coefficient of the gait signal based on the fluctuation function; The monitoring and analysis module compares the foot electrostatic signals collected in real time with the gait fingerprint feature library during real-time gait monitoring to determine whether there is an abnormal gait; The correction output module is used to generate a gait correction signal based on the gait fingerprint feature when an abnormal gait is detected.

6. The abnormal gait correction device based on foot electrostatic gait signal according to claim 5, characterized in that: The stability calculation unit comprises: A signal decomposition subunit, used for decomposing the gait signal into a plurality of intrinsic mode function components by using an empirical mode decomposition method; An energy calculation subunit, used to calculate the energy distribution of each intrinsic mode function component; The stability calculation subunit is used to calculate the gait stability index based on the energy distribution.

7. An intelligent guiding crutch for abnormal gait correction based on electrostatic gait signals of the foot, characterized in that: include: The crutch body; An abnormal gait correction device provided on a crutch body, wherein the abnormal gait correction device is the abnormal gait correction device according to any one of claims 5 or 6; A prompting device is arranged on the crutch body, and the prompting device is connected to the correction output module of the abnormal gait correction device.

8. The intelligent guiding crutch for abnormal gait correction based on foot electrostatic gait signals according to claim 7, characterized in that: The prompting device includes a voice prompter and / or a vibration prompter.

9. The intelligent guiding crutch for abnormal gait correction based on foot electrostatic gait signals according to claim 7, characterized in that: Also includes: A power module, which is disposed in the crutch body and is used to supply power to the abnormal gait correction device and the prompt device; A switch button is arranged on the crutch body and is electrically connected to the power module.

10. An abnormal gait correction intelligent guidance method based on the abnormal gait correction intelligent guidance crutch according to any one of claims 7 to 9, characterized in that: include: Collect the user's initial gait information and establish the user's gait fingerprint characteristics based on the user's gait information in a normal walking state; Real-time gait monitoring, comparing and analyzing the user's gait information collected in real time with the user's gait fingerprint features; Determine abnormal gait: if the deviation between any gait parameter in the real-time collected user gait information and the corresponding parameter in the user gait fingerprint feature exceeds a predetermined threshold, it is determined to be an abnormal gait; Output correction prompts. According to the type of abnormal gait, the abnormal gait correction device generates corresponding gait correction signals and transmits them to the prompt device, which outputs correction prompt information.

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