Wearable foot joint activity monitoring system and method

Abstract

The invention discloses a wearable foot joint activity monitoring system and method. The system comprises a joint activity monitoring module, a body balance and load monitoring module, a gait information acquisition module, a power supply module, a communication module, a data processing module, a storage module and a mobile terminal. The joint activity monitoring module is responsible for collecting joint activity information of a user and sending the joint activity information to the data processing module; the body balance and load monitoring module is used for collecting balance information and load information of a user and sending the related information to the data processing module; the gait information acquisition module is used for acquiring gait information of a user and sending the gait information to the data processing module; the joint movement information, the balance load information and the gait information are processed through a data processing module to obtain a foot joint movement monitoring result, and the foot joint movement monitoring result is sent to a mobile terminal through a communication module to be stored and displayed.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition and processing, and in particular to a wearable foot joint activity monitoring system and method. Background Art

[0002] With the improvement of living standards, people's demand for sports status monitoring and sports function monitoring is growing. Joint range of motion, proprioceptive balance, and weight-bearing exercise are important components of post-injury status monitoring of the foot and ankle. Reasonable weight-bearing, effective proprioceptive balance, and joint range of motion monitoring can promote local bone and soft tissue healing, accelerate the process of functional recovery, and avoid secondary injuries to the foot and ankle. At present, there are few targeted monitoring products for foot and ankle injuries. The existing weight-bearing monitoring insoles and lower limb weight-bearing training systems mainly realize real-time weight-bearing monitoring and feedback of lower limb bone injuries, and fail to meet the particularity of foot and ankle monitoring. Therefore, it is still necessary to develop a multifunctional foot and ankle health monitoring system that can cope with complex activity states, in order to help users achieve efficient monitoring, management, and self-management of foot and ankle health conditions. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a wearable foot joint activity monitoring system and method, which can cope with the foot and ankle health monitoring system in complex activity states, so as to help users achieve efficient monitoring, management and self-management of foot and ankle health conditions.

[0004] In order to solve the above technical problems, the first aspect of the embodiment of the present invention discloses a wearable foot joint activity monitoring system, the system includes a joint activity monitoring module, a body balance and weight monitoring module, a gait information collection module, a power supply module, a communication module, a data processing module, a storage module and a mobile terminal;

[0005] The joint activity monitoring module is data-connected to the data processing module and is used to collect the user's joint activity information;

[0006] The body balance and weight monitoring module is data-connected to the data processing module and is used to collect the balance information and weight information of the user;

[0007] The gait information collection module is data-connected to the data processing module and is used to collect the user's gait information;

[0008] The power supply module is data-connected with the joint range of motion monitoring module, the body balance and weight-bearing monitoring module, the gait information acquisition module and the communication module, and is used to supply power to the joint range of motion monitoring module, the body balance and weight-bearing monitoring module, the gait information acquisition module and the communication module;

[0009] The communication module is data-connected with the power supply module and the data processing module, and is used to send the data of the data processing module to the mobile terminal;

[0010] The data processing module is data-connected with the joint activity monitoring module, the body balance and weight-bearing monitoring module, the gait information acquisition module and the communication module for performing data processing to obtain joint activity monitoring results;

[0011] The storage module is data-connected to the data processing module and is used to store data of the data processing module;

[0012] The mobile terminal is used to store and display the joint activity monitoring results of the data processing module.

[0013] As an optional implementation, in the first aspect of the embodiment of the present invention, the joint range of motion monitoring module includes a first monitoring unit and a second monitoring unit;

[0014] The first monitoring unit is data-connected with the communication module and the power supply module, and is used to collect the user's left ankle joint activity angle information;

[0015] The second monitoring unit is data-connected with the communication module and the power supply module, and is used to collect the user's right ankle joint activity angle information.

[0016] As an optional implementation, in the first aspect of the embodiment of the present invention, the body balance and weight monitoring module includes a body balance monitoring unit and a weight monitoring unit;

[0017] The body balance monitoring unit is data-connected with the communication module and the power supply module, and is used to collect the user's body balance information;

[0018] The load monitoring unit is data-connected to the communication module and the power supply module, and is used to collect the user's load parameter information.

[0019] A second aspect of an embodiment of the present invention discloses a wearable foot joint activity monitoring method, the method comprising:

[0020] S1, using a joint activity monitoring module to collect joint activity information of a user, and sending the joint activity information to a data processing module;

[0021] The joint movement information includes left ankle joint movement angle information and right ankle joint movement angle information;

[0022] S2, using the body balance and weight monitoring module to collect the user's body balance information and weight parameter information, and sending the body balance information and weight parameter information to the data processing module;

[0023] S3, using a gait information collection module to collect the user's gait information, and sending the gait information to a data processing module;

[0024] S4, using the data processing module to process the joint activity information, the body balance information, the weight parameter information and the gait information to obtain a foot joint activity monitoring result, and using a communication module to send the foot joint activity monitoring result to a mobile terminal;

[0025] S5, using the mobile terminal to store and display the joint activity monitoring results.

[0026] As an optional implementation, in the second aspect of the embodiment of the present invention, the data processing module is used to process the joint activity information, the body balance information, the weight parameter information and the gait information to obtain the foot joint activity monitoring result, including:

[0027] S41, extracting features from the joint movement information to obtain joint movement feature information;

[0028] S42, extracting features from the body balance information to obtain body balance feature parameter information;

[0029] S43, extracting features from the load parameter information to obtain load feature parameter information;

[0030] S44, extracting features from the gait information to obtain gait feature information;

[0031] S45, fusing the joint movement feature information, the body balance feature parameter information, the weight-bearing feature parameter information and the gait feature information to obtain fused feature information;

[0032] S46, using the fusion feature information, training a preset joint activity monitoring model to obtain an optimized joint activity monitoring model;

[0033] S47, using the optimized joint activity monitoring model, processing the joint activity information, body balance information, weight parameter information and gait information to be processed to obtain foot joint activity monitoring results.

[0034] As an optional implementation, in the second aspect of the embodiment of the present invention, the extracting features of the joint movement information to obtain the joint movement feature information includes:

[0035] S411, extracting features of the left ankle joint activity angle information to obtain left ankle joint activity parameter information;

[0036] S412, extracting features of the right ankle joint activity angle information to obtain right ankle joint activity parameter information;

[0037] S413, fusing the left ankle joint activity parameter information and the right ankle joint activity parameter information to obtain joint activity feature information.

[0038] As an optional implementation, in the second aspect of the embodiment of the present invention, the feature extraction of the left ankle joint activity angle information to obtain the left ankle joint activity parameter information includes:

[0039] S4111, performing empirical mode decomposition on the left ankle joint activity angle information to obtain n IMF component signals and a residual signal;

[0040] S4112, processing the n IMF component signals to obtain k high-frequency components and m low-frequency components;

[0041] S4113, performing wavelet threshold denoising on the k high-frequency components to obtain k denoised high-frequency components;

[0042] S4114, reconstruct the k high-frequency components after denoising, the residual signal and the m low-frequency components to obtain left ankle joint activity parameter information.

[0043] As an optional implementation, in the second aspect of the embodiment of the present invention, the feature extraction of the body balance information to obtain the body balance feature parameter information includes:

[0044] S421, using a sampling processing model, processing the body balance information to obtain a first sampling signal;

[0045] The sampling processing model expression is:

[0046] S i =P i x(t)

[0047] In the formula, S i is the i-th first sampling signal, which is the result of processing the body balance information within the i-th step, i = 1, 2, ..., N, rect() represents the rectangular function, δ(t) is the unit impulse signal, T s is the preset sampling period, T p is the time length of each step, and t is the time variable;

[0048] S422, using a signal superposition model, superimposing the N first sampling signals to obtain body balance characteristic parameter information;

[0049] The signal superposition model expression is:

[0050]

[0051] In the formula, h i (t) is the i-th reconstruction filter, i = 1, 2, ..., N, * represents convolution, and y(t) is the body balance feature parameter information.

[0052] As an optional implementation manner, in the second aspect of the embodiment of the present invention, the feature extraction of the load parameter information to obtain the load feature parameter information includes:

[0053] S431, using a preset feature extraction model, performing feature extraction on the load parameter information to obtain first load parameter information;

[0054] The feature extraction model expression is:

[0055]

[0056] Wherein, t represents time, ω represents angular frequency, x() represents load parameter information, τ represents delay, W(t,ω) is the first load parameter information, and * represents conjugation;

[0057] S432, using a preset filtering model, filtering the first load parameter information W(t,ω) to obtain load characteristic parameter information

[0058] The preset filtering model expression is:

[0059]

[0060] Where W(τ,θ) represents the first load parameter information, W1(t-τ,ω-θ) is the two-dimensional template, G σs and G σr are two Gaussian functions, representing the spatial domain kernel and the pixel domain kernel respectively, τ is the time domain variable, and θ is the frequency domain variable;

[0061] G σs and G σr The specific expression is:

[0062]

[0063] In the formula, a and b represent the horizontal and vertical coordinates of the input pixel, i and j represent the coordinates of the center of the two-dimensional template, and σ s , σ r Represents the standard deviation of the Gaussian function.

[0064] As an optional implementation, in the second aspect of the embodiment of the present invention, the step of extracting features from the gait information to obtain gait feature information includes:

[0065] S441, processing the gait information to obtain first motion state information:

[0066]

[0067] where x n (m) is the gait information, X n (k) is the first motion state information, N2 is the length of the motion state information;

[0068] S442, processing the first motion state information to obtain second motion state information;

[0069]

[0070] where p n (k) is the second motion state information, * indicates taking conjugate;

[0071] S443, filtering the second motion state information to obtain third motion state information X(k), using a filter H m (k) is:

[0072]

[0073] The filter is a triangular filter bank with M filters, and its center frequency is recorded as f(m), m = 1, 2, ..., M,

[0074] S444, processing the third motion state information to obtain gait feature information;

[0075] The gait feature information expression is:

[0076] θ(k)=ln[|X(k)| 2 H m (k)]k=1,2,…,M

[0077] Where θ(k) is the gait feature information.

[0078] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0079] The present invention provides a wearable foot joint activity monitoring system and method, which can collect data such as the user's ankle joint activity, posture balance, weight-bearing condition, gait performance, etc., upload the data to a terminal device, and perform data analysis to obtain foot joint activity detection results, thereby effectively guiding users to perform exercise and avoid ankle injuries. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0081] Figure 1 is a structural schematic diagram of a wearable foot joint activity monitoring system disclosed in an embodiment of the present invention;

[0082] Figure 2 is a schematic diagram of a wearable shoe disclosed in an embodiment of the present invention;

[0083] Figure 3 It is a flow chart of a wearable foot joint activity monitoring method disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0084] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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.

[0085] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0086] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0087] The present invention discloses a wearable foot joint activity monitoring system and method, the system includes a joint activity monitoring module, a body balance and weight-bearing monitoring module, a gait information acquisition module, a power supply module, a communication module, a data processing module, a storage module and a mobile terminal; the joint activity monitoring module is responsible for collecting the user's joint activity information, and sending the joint activity information to the data processing module; the body balance and weight-bearing monitoring module is used to collect the user's balance information and weight-bearing information, and the relevant information is sent to the data processing module; the gait information acquisition module is used to collect the user's gait information, and the gait information is sent to the data processing module; the data processing module is used to process the joint activity information, the balance and weight-bearing information and the gait information to obtain the foot joint activity monitoring results, and the communication module is used to send the foot joint activity monitoring results to the mobile terminal for storage and display. The following are detailed descriptions.

[0088] Embodiment 1

[0089] See also Figure 1 , Figure 1 is a schematic diagram of the structure of a wearable foot joint activity monitoring system disclosed in an embodiment of the present invention. Figure 1 The wearable foot joint activity monitoring system described is used to monitor the health of the foot and ankle in complex activity states, helping users to achieve efficient monitoring and self-management of the health of the foot and ankle, which is not limited in the embodiments of the present invention. Figure 1 As shown, the wearable foot joint activity monitoring system includes a joint activity monitoring module, a body balance and weight monitoring module, a gait information acquisition module, a power supply module, a communication module, a data processing module, a storage module and a mobile terminal;

[0090] The joint activity monitoring module is data-connected to the data processing module and is used to collect the user's joint activity information;

[0091] The body balance and weight monitoring module is data-connected to the data processing module and is used to collect the balance information and weight information of the user;

[0092] The gait information collection module is data-connected to the data processing module and is used to collect the user's gait information;

[0093] The power supply module is data-connected with the joint range of motion monitoring module, the body balance and weight-bearing monitoring module, the gait information acquisition module and the communication module, and is used to supply power to the joint range of motion monitoring module, the body balance and weight-bearing monitoring module, the gait information acquisition module and the communication module;

[0094] The communication module is data-connected with the power supply module and the data processing module, and is used to send the data of the data processing module to the mobile terminal;

[0095] The data processing module is data-connected with the joint activity monitoring module, the body balance and weight-bearing monitoring module, the gait information acquisition module and the communication module for performing data processing to obtain joint activity monitoring results;

[0096] The storage module is data-connected to the data processing module and is used to store data of the data processing module;

[0097] The mobile terminal is used to store and display the joint activity monitoring results of the data processing module.

[0098] Optionally, a vibration alarm unit is set in the data processing module to compare the collected joint movement information, body balance information, weight parameter information, and gait information with preset thresholds. When the preset thresholds are exceeded, vibration reminds the wearer of abnormal balance, excessive weight, and improper exercise.

[0099] The storage module is data-connected to the data processing module and is used to store data of the data processing module;

[0100] The mobile terminal is used to store and display the joint activity monitoring results of the data processing module.

[0101] The shoes interact with corresponding mobile terminals (such as mobile phones) with the help of communication modules to help provide real-time feedback on the wearer's body balance, weight-bearing conditions, and joint movement performance, allowing for efficient foot and ankle exercise and weight-bearing health management.

[0102] The joint range of motion monitoring module, body balance and weight monitoring module, gait information collection module, power supply module, communication module, data processing module and storage module are integrated into the wearable shoe, such as Figure 2 As shown in Figure 2, the connection relationship between each module is as follows: Figure 1 The wearable footwear includes a shoe body that can tightly wrap the human foot and upper ankle, as well as a functional layer of the shoe body and sole, to achieve all-round monitoring of the range of motion, sports performance and weight-bearing conditions of the foot and ankle joints. Figure 2 This is only a schematic diagram, and the specific composition structure and position relationship of each module are not limited in this embodiment.

[0103] There are two functional layers on the bottom of the shoe. The bottom layer is the motion power supply layer (power supply module), which includes piezoelectric film, graphene conductive coil and flexible battery. It uses the kinetic energy generated by human body movement and vibration, and converts and stores it into electrical energy that can operate the entire shoe body. The upper layer is placed on the body balance and weight monitoring module, which is composed of a micro-electromechanical system and connection components to track the body balance information and weight parameter information when the human body is moving or at rest.

[0104] Optionally, the joint range of motion monitoring module includes a first monitoring unit and a second monitoring unit;

[0105] The first monitoring unit is data-connected with the communication module and the power supply module, and is used to collect the user's left ankle joint activity angle information;

[0106] The second monitoring unit is data-connected with the communication module and the power supply module, and is used to collect the user's right ankle joint activity angle information.

[0107] The first monitoring unit and the second monitoring unit are flexible piezoelectric sensors, which are used to collect the user's joint activity angles during internal and external rotation (left ankle joint activity angle information and right ankle joint activity angle information).

[0108] Optionally, the body balance and weight monitoring module includes a body balance monitoring unit and a weight monitoring unit;

[0109] The body balance monitoring unit is data-connected with the communication module and the power supply module, and is used to collect the user's body balance information;

[0110] The load monitoring unit is data-connected to the communication module and the power supply module, and is used to collect the user's load parameter information.

[0111] The body balance monitoring unit collects data from a balance test instrument and is mainly composed of a force measuring platform, an amplifying filter, a single chip microcomputer, a computer device, a display device and other equipment, which is not limited in this embodiment.

[0112] The load monitoring unit uses sensors to collect changes in the user's foot pressure, thereby reflecting the user's body load.

[0113] It can be seen that the present invention provides a wearable foot joint activity monitoring system and method, which can collect data such as the user's foot and ankle joint activity, posture balance state, weight-bearing condition, gait performance, etc., upload it to a terminal device, and perform data analysis to obtain foot joint activity detection results, thereby effectively guiding users to perform exercise and avoid foot and ankle injuries.

[0114] Embodiment 2

[0115] See also Figure 3 , Figure 3 is a flow chart of a wearable foot joint activity monitoring method disclosed in an embodiment of the present invention. Figure 3 The wearable foot joint activity monitoring method described is applied to foot and ankle health monitoring in complex activity states, helping users to achieve efficient monitoring and self-management of foot and ankle health conditions, which is not limited in the embodiments of the present invention. Figure 3 As shown, the wearable foot joint activity monitoring method includes:

[0116] S1, using a joint activity monitoring module to collect joint activity information of a user, and sending the joint activity information to a data processing module;

[0117] The joint movement information includes left ankle joint movement angle information and right ankle joint movement angle information;

[0118] S2, using the body balance and weight monitoring module to collect the user's body balance information and weight parameter information, and sending the body balance information and weight parameter information to the data processing module;

[0119] S3, using a gait information collection module to collect the user's gait information, and sending the gait information to a data processing module;

[0120] S4, using the data processing module to process the joint activity information, the body balance information, the weight parameter information and the gait information to obtain a foot joint activity monitoring result, and using a communication module to send the foot joint activity monitoring result to a mobile terminal;

[0121] The data from the data processing module and the foot joint activity monitoring results are also sent to the storage module for storage.

[0122] S5, using the mobile terminal to store and display the joint activity monitoring results.

[0123] Optionally, the data processing module is used to process the joint activity information, the body balance information, the weight parameter information and the gait information to obtain a foot joint activity monitoring result, including:

[0124] S41, extracting features from the joint movement information to obtain joint movement feature information;

[0125] S42, extracting features from the body balance information to obtain body balance feature parameter information;

[0126] S43, extracting features from the load parameter information to obtain load feature parameter information;

[0127] S44, extracting features from the gait information to obtain gait feature information;

[0128] S45, fusing the joint movement feature information, the body balance feature parameter information, the weight-bearing feature parameter information and the gait feature information to obtain fused feature information;

[0129] S46, using the fusion feature information, training a preset joint activity monitoring model to obtain an optimized joint activity monitoring model;

[0130] S47, using the optimized joint activity monitoring model, processing the joint activity information, body balance information, weight parameter information and gait information to be processed to obtain foot joint activity monitoring results.

[0131] The joint activity monitoring model is: a convolutional long short-term memory network (ConvLSTM) combined with an LSTM network. ConvLSTM introduces convolution operations on the basis of LSTM, thereby improving the processing capability of time series data.

[0132] Model structure:

[0133] Convolutional Long Short-Term Memory Network (ConvLSTM): ConvLSTM can capture spatiotemporal features more effectively by introducing convolution operations in LSTM units. Its calculation formula is as follows:

[0134] Forget Gate:

[0135]

[0136] f t : Forget gate output, controls the degree of information forgetting; σ: Sigmoid activation function, compresses the output value between 0 and 1; W xf : Input data; x t : Input data at the current moment, including physiological parameters such as heart rate, blood oxygen, and respiratory rate; W hf : The hidden state of the previous moment; h t-1 The convolution kernel weight associated with the forget gate. W cf :Unit status; c t-1 : The unit state at the last moment, storing the accumulation of past information. f : Bias term of forget gate, helps adjust the output of forget gate.

[0137] Input Gate:

[0138]

[0139] i t : Input gate output, controlling the degree of introduction of new information; W xi : Input data x t The convolution kernel weight associated with the input gate; W hi : The hidden state of the previous moment; h t-1 The convolution kernel weight associated with the input gate; W ci :Unit status c t-1 The weight associated with the input gate; b i : Bias term for input gate.

[0140] Output Gate:

[0141]

[0142] o t : Output gate output, controlling the cell state; c t : How much information is passed to the hidden state; W xo : Input data; x t The convolution kernel weight associated with the output gate; W ho : The hidden state of the previous moment; h t-1 The convolution kernel weight associated with the output gate; W co :Unit status c t The weight associated with the output gate; b o : Bias term for the output gate.

[0143] Unit status update:

[0144]

[0145] c t : The unit state at the current moment; tanh: Hyperbolic tangent function, compressing the value between -1 and 1. W xc : Input data x t Convolution kernel weights associated with the cell state update. W hc : The hidden state of the previous moment; h t-1 Convolution kernel weights associated with unit state updates; b c : Bias term for unit state update.

[0146] Hide status updates:

[0147]

[0148] h t : The hidden state at the current moment, including the physiological parameter characteristics at the current moment.

[0149] o t : The output of the output gate determines the cell state c t How much information is passed to the hidden state.

[0150] Optionally, the extracting features of the joint movement information to obtain joint movement feature information includes:

[0151] S411, extracting features of the left ankle joint activity angle information to obtain left ankle joint activity parameter information;

[0152] S412, extracting features of the right ankle joint activity angle information to obtain right ankle joint activity parameter information;

[0153] S413, fusing the left ankle joint activity parameter information and the right ankle joint activity parameter information to obtain joint activity feature information.

[0154] Integrating the left ankle joint activity parameter information and the right ankle joint activity parameter information to obtain fusion parameter information;

[0155] The fusion parameter information expression is:

[0156]

[0157] Among them, Z is the fusion parameter information, Z1 is the left ankle joint activity parameter information, Z2 is the right ankle joint activity parameter information, α1 and a2 are weight coefficients, α1+a2=1;

[0158] Centralizing the fusion parameter information to obtain centralized parameter information;

[0159] The centralization parameter information expression is:

[0160] Z1=Zu

[0161] Among them, Z1 is the centralized parameter information, z i is the i-th element in Z, with a total of n elements;

[0162] Performing covariance calculation on the centralized parameter information to obtain a covariance matrix;

[0163] Performing eigenvalue decomposition on the covariance matrix to obtain m eigenvalues;

[0164] Sort the m eigenvalues ​​and extract the eigenvectors corresponding to the first k eigenvalues;

[0165] Using the eigenvector reconstruction model, the eigenvectors corresponding to the first k eigenvalues ​​are processed to obtain joint movement feature information;

[0166] The feature vector reconstruction model expression is:

[0167] U=[p1Z1,p2Z1,…,p k Z1]

[0168] Among them, U is the characteristic information of joint movement, [p1,p2,…,p k ] is the eigenvector corresponding to the first k eigenvalues.

[0169] Optionally, the feature extraction of the left ankle joint activity angle information to obtain left ankle joint activity parameter information includes:

[0170] S4111, performing empirical mode decomposition on the left ankle joint activity angle information to obtain n IMF component signals and a residual signal;

[0171] S4112, processing the n IMF component signals to obtain k high-frequency components and m low-frequency components;

[0172] S4113, performing wavelet threshold denoising on the k high-frequency components to obtain k denoised high-frequency components;

[0173] S4114, reconstructing the k high-frequency components after denoising, the residual signal and the m low-frequency components to obtain left ankle joint activity parameter information.

[0174] The method for extracting the right ankle joint activity angle information is the same as the method for extracting the left ankle joint activity angle information.

[0175] Optionally, the extracting features of the body balance information to obtain body balance feature parameter information includes:

[0176] S421, using a sampling processing model, processing the body balance information to obtain a first sampling signal;

[0177] The sampling processing model expression is:

[0178] S i =P i x(t)

[0179] In the formula, S i is the i-th first sampling signal, which is the result of processing the body balance information within the i-th step, i = 1, 2, ..., N, rect() represents the rectangular function, δ(t) is the unit impulse signal, T s is the preset sampling period, T p is the time length of each step, and t is the time variable;

[0180] S422, using a signal superposition model, superimposing the N first sampling signals to obtain body balance characteristic parameter information;

[0181] The signal superposition model expression is:

[0182]

[0183] In the formula, h i (t) is the i-th reconstruction filter, i = 1, 2, ..., N, * represents convolution, and y(t) is the body balance feature parameter information.

[0184] Optionally, the extracting features of the load parameter information to obtain the load feature parameter information includes:

[0185] S431, using a preset feature extraction model, performing feature extraction on the load parameter information to obtain first load parameter information;

[0186] The feature extraction model expression is:

[0187]

[0188] Wherein, t represents time, ω represents angular frequency, x() represents load parameter information, τ represents delay, W(t,ω) is the first load parameter information, and * represents conjugation;

[0189] S432, using a preset filtering model, filtering the first load parameter information W(t,ω) to obtain load characteristic parameter information

[0190] The preset filtering model expression is:

[0191]

[0192] Where W(τ,θ) represents the first load parameter information, W1(t-τ,ω-θ) is the two-dimensional template, G σs and G σr are two Gaussian functions, representing the spatial domain kernel and the pixel domain kernel respectively, τ is the time domain variable, and θ is the frequency domain variable;

[0193] and The specific expression is:

[0194]

[0195] In the formula, a and b represent the horizontal and vertical coordinates of the input pixel, i and j represent the coordinates of the center of the two-dimensional template, and σ s , σ r Represents the standard deviation of the Gaussian function.

[0196] Optionally, extracting features from the gait information to obtain gait feature information includes:

[0197] S441, processing the gait information to obtain first motion state information:

[0198]

[0199] where x n (m) is the gait information, X n (k) is the first motion state information, N2 is the length of the motion state information;

[0200] S442, processing the first motion state information to obtain second motion state information;

[0201]

[0202] where p n (k) is the second motion state information, * indicates taking conjugate;

[0203] S443, filtering the second motion state information to obtain third motion state information X(k), using a filter H m (k) is:

[0204]

[0205] The filter is a triangular filter bank with M filters, and its center frequency is recorded as f(m), m = 1, 2, ..., M,

[0206] S444, processing the third motion state information to obtain gait feature information;

[0207] The gait feature information expression is:

[0208] θ(k)=ln[|X(k)| 2 H m (k)]k=1,2,…,M

[0209] Where θ(k) is the gait feature information.

[0210] Optionally, in step S45, the joint movement characteristic information, the body balance characteristic parameter information, the weight-bearing characteristic parameter information and the gait characteristic information are fused to obtain fused characteristic information; the specific fusion method is:

[0211] Fusing joint activity characteristic information and body balance characteristic parameters to obtain a first fusion parameter;

[0212] fusing the load characteristic parameter information and the gait characteristic information to obtain a second fusion parameter;

[0213] The first fusion parameter and the second fusion parameter are fused to obtain fusion feature information.

[0214] The fusion method used here is:

[0215] The two feature matrices that need to be fused are X and Y, X is an n′×m′ dimensional matrix, Y is a p×m′ dimensional matrix, m′ represents the number of samples, n′ and p represent the dimensions of the two features, and the two matrices are projected to 1 dimension for linear representation, corresponding to the projection vectors a1 and b1 respectively, then the feature matrix after projection becomes:

[0216]

[0217] The purpose is to maximize the correlation coefficient between X' and Y', so as to obtain the projection vectors a1 and b1 when the correlation coefficient is maximum, that is:

[0218]

[0219] Before projection, the data is standardized. The purpose of standardization is to make the mean of the data 0 and the variance 1. We can get:

[0220] cov(X',Y')=cov(a1 T X,b1 T Y)=E(<a1 T X,b1 T Y>)=E((a1 T X)(b1 T Y) T )=a1 T E(XY T )b1

[0221]

[0222] D(X)=cov(X,X)=E(X T X)

[0223] D(Y)=cov(Y,Y)=E(Y T Y)

[0224] cov(X,Y)=E(XY T ),cov(Y,X)=E(YX T )

[0225] S XX =cov(X,X), then the solution objective is transformed into:

[0226]

[0227] Step 1: Calculate the variance S of X and Y XX and S YY , the covariance S of XY and YX XY =S YX T ;

[0228] Step 2: Calculate the matrix

[0229] Step 3: Solve the singular values ​​of M′ to obtain the largest singular value and its preceding and following singular vectors u, v;

[0230] Step 4: The projection vectors a1 and b1 of X and Y are:

[0231]

[0232] The fused feature vector is

[0233] It can be seen that the present invention provides a wearable foot joint activity monitoring system and method, which can collect data such as the user's foot and ankle joint activity, posture balance state, weight-bearing condition, gait performance, etc., upload it to a terminal device, and perform data analysis to obtain foot joint activity detection results, thereby effectively guiding users to perform exercise and avoid foot and ankle injuries.

[0234] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0235] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0236] Finally, it should be noted that the wearable foot joint activity monitoring system and method disclosed in the embodiments of the present invention disclose only the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wearable foot joint activity monitoring system, characterized in that: The system includes a joint range of motion monitoring module, a body balance and weight-bearing monitoring module, a gait information acquisition module, a power supply module, a communication module, a data processing module, a storage module and a mobile terminal; The joint activity monitoring module is data-connected to the data processing module and is used to collect the user's joint activity information; The body balance and weight monitoring module is data-connected to the data processing module and is used to collect the balance information and weight information of the user; The gait information collection module is data-connected to the data processing module and is used to collect the user's gait information; The power supply module is data-connected with the joint range of motion monitoring module, the body balance and weight-bearing monitoring module, the gait information acquisition module and the communication module, and is used to supply power to the joint range of motion monitoring module, the body balance and weight-bearing monitoring module, the gait information acquisition module and the communication module; The communication module is data-connected with the power supply module and the data processing module, and is used to send the data of the data processing module to the mobile terminal; The data processing module is data-connected with the joint activity monitoring module, the body balance and weight-bearing monitoring module, the gait information acquisition module and the communication module for performing data processing to obtain joint activity monitoring results; The storage module is data-connected to the data processing module and is used to store data of the data processing module; The mobile terminal is used to store and display the joint activity monitoring results of the data processing module.

2. The wearable foot joint activity monitoring system according to claim 1, characterized in that: The joint range of motion monitoring module includes a first monitoring unit and a second monitoring unit; The first monitoring unit is data-connected with the communication module and the power supply module, and is used to collect the user's left ankle joint activity angle information; The second monitoring unit is data-connected with the communication module and the power supply module, and is used to collect the user's right ankle joint activity angle information.

3. The wearable foot joint activity monitoring system according to claim 1, characterized in that: The body balance and weight monitoring module includes a body balance monitoring unit and a weight monitoring unit; The body balance monitoring unit is data-connected with the communication module and the power supply module, and is used to collect the user's body balance information; The load monitoring unit is data-connected to the communication module and the power supply module, and is used to collect the user's load parameter information.

4. A wearable foot joint activity monitoring method, characterized in that: Applied to the wearable foot joint activity monitoring system according to any one of claims 1 to 3, the method comprising: S1, using a joint activity monitoring module to collect joint activity information of a user, and sending the joint activity information to a data processing module; The joint movement information includes left ankle joint movement angle information and right ankle joint movement angle information; S2, using the body balance and weight monitoring module to collect the user's body balance information and weight parameter information, and sending the body balance information and weight parameter information to the data processing module; S3, using a gait information collection module to collect the user's gait information, and sending the gait information to a data processing module; S4, using the data processing module to process the joint activity information, the body balance information, the weight parameter information and the gait information to obtain a foot joint activity monitoring result, and using a communication module to send the foot joint activity monitoring result to a mobile terminal; S5, using the mobile terminal to store and display the joint activity monitoring results.

5. The wearable foot joint activity monitoring method according to claim 4, characterized in that: The data processing module is used to process the joint activity information, the body balance information, the weight parameter information and the gait information to obtain the foot joint activity monitoring result, including: S41, extracting features from the joint movement information to obtain joint movement feature information; S42, extracting features from the body balance information to obtain body balance feature parameter information; S43, extracting features from the load parameter information to obtain load feature parameter information; S44, extracting features from the gait information to obtain gait feature information; S45, fusing the joint movement feature information, the body balance feature parameter information, the weight-bearing feature parameter information and the gait feature information to obtain fused feature information; S46, using the fusion feature information, training a preset joint activity monitoring model to obtain an optimized joint activity monitoring model; S47, using the optimized joint activity monitoring model, processing the joint activity information, body balance information, weight parameter information and gait information to be processed to obtain foot joint activity monitoring results.

6. The wearable foot joint activity monitoring method according to claim 5, characterized in that: The step of extracting features from the joint movement information to obtain joint movement feature information includes: S411, extracting features of the left ankle joint activity angle information to obtain left ankle joint activity parameter information; S412, extracting features of the right ankle joint activity angle information to obtain right ankle joint activity parameter information; S413, fusing the left ankle joint activity parameter information and the right ankle joint activity parameter information to obtain joint activity feature information.

7. The wearable foot joint activity monitoring method according to claim 6, characterized in that: The feature extraction of the left ankle joint activity angle information to obtain the left ankle joint activity parameter information includes: S4111, performing empirical mode decomposition on the left ankle joint activity angle information to obtain n IMF component signals and a residual signal; S4112, processing the n IMF component signals to obtain k high-frequency components and m low-frequency components; S4113, performing wavelet threshold denoising on the k high-frequency components to obtain k denoised high-frequency components; S4114, reconstruct the k high-frequency components after denoising, the residual signal and the m low-frequency components to obtain left ankle joint activity parameter information.

8. The wearable foot joint activity monitoring method according to claim 5, characterized in that: The extracting features of the body balance information to obtain body balance feature parameter information includes: S421, using a sampling processing model, processing the body balance information to obtain a first sampling signal; The sampling processing model expression is: S i =P i x(t) In the formula, S i is the i-th first sampling signal, which is the result of processing the body balance information within the i-th step, i = 1, 2,, N, rect() represents the rectangular function, δ(t) is the unit impulse signal, T s is the preset sampling period, T p is the time length of each step, and t is the time variable; S422, using a signal superposition model, superimposing the N first sampling signals to obtain body balance characteristic parameter information; The signal superposition model expression is: In the formula, h i (t) is the i-th reconstruction filter, i = 1, 2,, N, * represents convolution, and y(t) is the body balance feature parameter information.

9. The wearable foot joint activity monitoring method according to claim 5, characterized in that: The step of extracting features from the load parameter information to obtain load feature parameter information includes: S431, using a preset feature extraction model, performing feature extraction on the load parameter information to obtain first load parameter information; The feature extraction model expression is: Wherein, t represents time, ω represents angular frequency, x() represents load parameter information, τ represents delay, W(t,ω) is the first load parameter information, and * represents conjugation; S432, using a preset filtering model, filtering the first load parameter information W(t,ω) to obtain load characteristic parameter information The preset filtering model expression is: Where W(τ,θ) represents the first load parameter information, W1(t-τ,ω-θ) is the two-dimensional template, and are two Gaussian functions, representing the spatial domain kernel and the pixel domain kernel respectively, τ is the time domain variable, and θ is the frequency domain variable; and The specific expression is: In the formula, a and b represent the horizontal and vertical coordinates of the input pixel, i and j represent the coordinates of the center of the two-dimensional template, and σ s , σ r Represents the standard deviation of the Gaussian function.

10. The wearable foot joint activity monitoring method according to claim 5, characterized in that: The step of extracting features from the gait information to obtain gait feature information includes: S441, processing the gait information to obtain first motion state information: where x n (m) is the gait information, X n (k) is the first motion state information, N2 is the length of the motion state information; S442, processing the first motion state information to obtain second motion state information; where p n (k) is the second motion state information, * indicates taking conjugate; S443, filtering the second motion state information to obtain third motion state information X(k), using a filter H m (k) is: The filter is a triangular filter bank with M filters, and its center frequency is recorded as f(m), m = 1, 2, ..., M, S444, processing the third motion state information to obtain gait feature information; The gait feature information expression is: θ(k)=ln[|X(k)| 2 H m (k)]k=1,2,,M Where θ(k) is the gait feature information.

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