An electrical stimulation intervention knee brace

Abstract

This invention discloses an electrical stimulation intervention knee brace, comprising: a signal anomaly judgment module, used to determine whether the muscle contraction pattern is abnormal during walking based on the acquired electromyographic signals; a standardization processing module, used to perform difference judgment processing on the acquired time series and the standard time series based on the mean and variance of the muscle contraction degree time series acquired by each electromyographic electrode channel and the corresponding standard muscle contraction time series, to obtain a standardized acquisition time series and a standardized standard time series; a cross-correlation function calculation module, used to calculate the cross-correlation function of the standardized acquisition time series and the standardized standard time series; a conversion module, used to convert the cross-correlation function into a continuous integral form function and calculate the maximum value of the continuous integral form function; and a determination module, used to determine the time difference between the test time signal and the standard time signal corresponding to the maximum value, and determine the time point and duration of stimulation application based on the time interpolation.

Description

Technical Field

[0001] This invention relates to the field of knee braces technology, and more particularly to an electrical stimulation intervention knee brace. Background Technology

[0002] The knee joint is a vital pivot joint in the lower limbs, participating in numerous lower limb activities such as walking, running, and jumping. When walking, the knee joint on one side can bear 3 to 4 times the body weight; during running and jumping, this can increase to 4 to 24 times. Due to the routine nature of walking, injuries, and variations in load, degenerative changes in the knee joint often occur, leading to cartilage degeneration and destruction, bone marrow edema, and osteophyte formation. This manifests as knee osteoarthritis, characterized by pain, stiffness, and limited range of motion. Consequently, patients experience a decline in overall mobility and quality of life. Due to knee inflammation, pain, or decreased proprioception, patients often habitually alter their walking posture to compensate for lower limb weakness or alleviate joint pain. While this change may alleviate symptoms to some extent, it also creates new problems, such as alterations in lower limb alignment, muscle contraction patterns, and joint stress distribution, resulting in increased abnormal stress on the joint and muscle co-contraction. This leads to new joint pain, abnormally increased local stress, and joint stiffness, creating a vicious cycle in the development of arthritis.

[0003] Knee braces, as a common auxiliary treatment, offer various benefits such as correcting joint alignment, reducing joint stress, improving joint stability, relieving swelling, and providing warmth. They are frequently used in clinical treatment and routine rehabilitation. Currently, most traditional knee braces on the market fall into two categories: First, those based on mechanical structure design primarily use external force support for correction and treatment, mainly achieving prevention and treatment by unloading or redistributing stress on the knee joint. Second, those incorporating magnetic therapy, heating elements, or traditional Chinese medicine external application to improve knee osteoarthritis symptoms, primarily providing adjunctive treatment by improving local blood circulation and promoting the absorption of inflammatory substances. Intelligent knee braces, on the other hand, primarily consist of intelligent monitoring devices, mainly monitoring parameters such as knee joint temperature, angle, and cadence. They assess the osteoarthritis status of the knee joint and the kinematic state of the lower limbs, transmitting the data in real time to doctors or rehabilitation therapists for appropriate adjustments and treatment.

[0004] In the process of developing this invention, the inventors discovered the following technical problem: Existing knee braces mainly provide passive support or correction through mechanical external force, often resulting in atrophy of the muscles around the joint or further reduction in proprioception. Long-term use exacerbates muscle atrophy, creating a vicious cycle and increasing the difficulty of rehabilitation. Summary of the Invention

[0005] This invention provides an electrical stimulation intervention knee brace to address the technical problem that existing knee braces often exacerbate muscle atrophy in the prevention and treatment of knee joint inflammation. Secondly, due to factors such as inflammation and pain, the contraction patterns of muscles around the knee joint change accordingly, which to some extent aggravates joint degeneration or inflammation. This invention aims to specifically correct abnormal muscle contraction patterns based on the muscle contraction patterns (muscle contraction degree and timing) in the normal gait cycle, thereby breaking the vicious cycle of disease development.

[0006] In view of this, embodiments of the present invention provide an electrical stimulation intervention knee joint brace, comprising:

[0007] The sEMG signal anomaly detection module is used to determine whether muscle contraction is abnormal during walking based on the collected sEMG signals.

[0008] The standardization module is used to standardize the acquired time series and the standard time series when judging abnormal muscle contraction, based on the mean and variance of the time series acquired by each electromyographic electrode channel and the corresponding standard time series, to obtain a standardized acquired time series and a standardized standard time series. The standardization process is implemented in the following way:

[0009] Where, L(n) i For each channel, the time series data is collected, R(n). i For each channel, the standard time series is defined by μ[L(n)i] and μ[R(n)i]. i σ[L(n)i] and σ[R(n)i] are the mean values ​​of the time series and the standardized time series collected from each channel, respectively. i [ ] represents the variance of the time series acquired by each channel and the standardized standard time series, respectively;

[0010] The cross-correlation function calculation module is used to calculate the cross-correlation function between the standardized acquired time series and the standardized standard time series. The cross-correlation function is as follows:

[0011]

[0012] The conversion module is used to convert the cross-correlation function into a continuous integral form function and calculate the maximum value of the continuous integral form function, which is as follows:

[0013]

[0014] r(t) i , l(t) iThe signal is a continuous signal before sampling, and τ is the timing difference between the time signal to be measured and the standard time signal.

[0015] The determination module is used to determine the timing difference between the measured time signal corresponding to the maximum value and the standard time signal, and to determine the time point and duration of the stimulus application based on the timing interpolation.

[0016] Furthermore, the electrical stimulation intervention knee joint brace also includes:

[0017] The approximation calculation module is used to establish feature vectors for each element of the time series collected within a preset time range and the corresponding element in the normally calibrated time series based on duration and intensity, and to calculate the cosine approximation of each element and the corresponding element.

[0018] The ratio determination module is used to compare the calculated cosine approximation with a preset approximation threshold. When the cosine approximation is less than the approximation threshold, the ratio of electrical stimulation is determined based on the ratio of the difference between the cosine approximation and the approximation threshold to the approximation threshold.

[0019] Furthermore, the approximation calculation module includes:

[0020] The standardization processing unit is used to standardize each element of the time series collected within a preset time range and the corresponding element in the normally calibrated time series, and to establish a feature vector for each standardized element to facilitate the calculation of the cosine approximation.

[0021] Furthermore, the electrical stimulation intervention knee joint brace also includes:

[0022] A gait information measuring device, wherein the gait information measuring unit is used to measure the user's gait information to determine a stride period;

[0023] Correspondingly, the sEMG signal anomaly judgment module is used to determine whether muscle contraction is abnormal during walking based on the sEMG signals collected within a stride period.

[0024] Furthermore, the gait information measuring device includes:

[0025] A microelectromechanical accelerometer is installed 10 cm above and below the patella on the anterolateral aspect of the thigh.

[0026] Furthermore, the electrical stimulation intervention knee joint brace also includes:

[0027] An electrical stimulator is used to apply low-frequency electrical stimulation to corresponding muscles using electrode patches according to the timing and duration of stimulation, thereby activating muscle contraction in the corresponding area. The electrode patches are respectively placed on the vastus medialis, rectus femoris, vastus lateralis, tibialis anterior, biceps femoris, semitendinosus and semimembranosus, medial and lateral sides of the gastrocnemius.

[0028] Furthermore, the electrical stimulation intervention knee joint brace also includes:

[0029] A knee brace, comprising: upper and lower movable arms, a fixing strap, and a joint hinge, wherein the joint hinge is movably mounted on the upper and lower movable arms, and the fixing strap is mounted on the upper and lower movable arms.

[0030] The electrical stimulation intervention knee brace provided in this embodiment of the invention includes an sEMG signal abnormality judgment module for judging whether muscle contraction is abnormal during walking based on the acquired sEMG signals; a standardization processing module for standardizing the acquired time series and standard time series based on the mean and variance of the time series acquired by each electromyography electrode channel and the corresponding standard time series when abnormal muscle contraction is judged; a cross-correlation function calculation module for calculating the cross-correlation function of the standardized acquired time series and the standardized standard time series; a conversion module for converting the cross-correlation function into a continuous integral form function and calculating the maximum value of the continuous integral form function; and a determination module for determining the time difference between the measured time signal and the standard time signal corresponding to the maximum value, and determining the time point and duration of stimulation based on the time interpolation. Multiple intelligent sensors can monitor lower limb kinematic parameters and neuroelectromyographic signals. Machine learning models can be used to determine the activation level, contraction timing, and contraction intensity of muscles around the knee joint. In cases of abnormality, the model can automatically identify the differences and release corresponding low-frequency current stimulation to correct the abnormal contraction patterns of muscles around the knee joint. This can enhance the strength of the patient's lower limb muscles, improve muscle contraction patterns, and prevent muscle atrophy. Attached Figure Description

[0031] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0032] Figure 1 This is a schematic diagram of the computing device in the electrical stimulation intervention knee joint brace provided in an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of the structure of the electrical stimulation intervention knee joint brace provided in an embodiment of the present invention;

[0034] In the figure: 1. Knee brace; 2. Multipolar surface electromyography electrode; 3. MEMS accelerometer; 4. Pre-processor signal processor; 5. Microcontroller; 6. Electrical stimulator; 7. Electrical stimulation electrode pads. Detailed Implementation

[0035] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0036] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0037] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0038] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0039] Figure 1 This is a schematic diagram of the computing device in the electrical stimulation intervention knee brace provided in an embodiment of the present invention. See also: Figure 1The electrical stimulation intervention knee brace includes: an sEMG signal abnormality judgment module, used to determine whether muscle contraction is abnormal during walking based on the acquired sEMG signals; and a standardization processing module, used to standardize the acquired time series and standard time series based on the mean and variance of the time series acquired by each electromyography electrode channel and the corresponding standard time series when judging abnormal muscle contraction, to obtain a standardized acquisition time series and a standardized standard time series. The standardization processing is implemented in the following way:

[0040]

[0041] Where, L(n) i For each channel, the time series data is collected, R(n). i For each channel, the standard time series is defined by μ[L(n)i] and μ[R(n)i]. i σ[L(n)i] and σ[R(n)i] are the mean values ​​of the time series and the standardized time series collected from each channel, respectively. i [These represent the variances of the time series acquired by each channel and the standardized standard time series, respectively; the cross-correlation function calculation module is used to calculate the cross-correlation function between the standardized acquired time series and the standardized standard time series, and the cross-correlation function is as follows:]

[0042]

[0043] The conversion module is used to convert the cross-correlation function into a continuous integral form function and calculate the maximum value of the continuous integral form function, which is as follows:

[0044]

[0045] r(t) i , l(t) i The signal is a continuous signal before sampling, and τ is the time difference between the time signal to be measured and the standard time signal. The determination module is used to determine the time difference between the time signal to be measured and the standard time signal corresponding to the maximum value, and to determine the time point and duration of the stimulus application based on the time interpolation.

[0046] In this embodiment, the electrical stimulation intervention knee brace can be optimized based on the original knee brace. Figure 2 This is a schematic diagram of the structure of the electrical stimulation intervention knee joint brace provided in an embodiment of the present invention. See also... Figure 2The electrical stimulation intervention knee brace may include: a knee brace comprising: upper and lower movable arms, a fixation strap, and a joint hinge. The joint hinge is movably mounted on the upper and lower movable arms, and the fixation strap is mounted on the upper and lower movable arms. This structure facilitates wearing and fixation for patients of different heights. Simultaneously, corresponding electronic devices can be fixed to the knee brace to achieve electrical stimulation intervention. The selection of the brace mainly considers two factors: first, selecting whether the lateral support is located on the medial or lateral side based on the knee joint varus / valgus data (X-type or O-type); second, selecting a support pattern to reduce interference with muscle contraction patterns.

[0047] The described electrical stimulation knee brace can detect the relevant muscles around the knee joint during walking using various configured sensors. In this embodiment, multi-channel bipolar surface electromyography (sEMG) electrodes can be used to collect electromyographic signals reflecting muscle contraction. Optionally, these electrodes can be positioned at relevant locations such as the vastus medialis, rectus femoris, vastus lateralis, tibialis anterior, biceps femoris, semitendinosus, semimembranosus, medial gastrocnemius, and lateral gastrocnemius. Furthermore, a microelectromechanical accelerometer (MEMS) is included, positioned 10 cm above and below the patella on the anterolateral aspect of the thigh. This placement allows for accurate measurement of whether the patient is currently walking and the determination of a complete walking cycle. Only the electromyographic signals collected by sEMG within a single walking cycle can accurately determine whether the muscle contraction during walking is normal; otherwise, incorrect judgments will occur. The MEMS accelerometer selected is the MPU6050, which features a 3-axis angular velocity sensor (gyroscope) with a sensitivity of 131 LSBs / ° / sec and a full-range sensing range of ±250, ±500, ±1000, and ±2000° / sec. The programmable control range is ±2g, ±4g, ±8g, and ±16g, meeting the measurement requirements. The surface electromyography sensor employs a multi-channel electrode system capable of acquiring 6–8 channels of signals. The electrode substrate uses gel-attached electrodes in a bipolar configuration, with a reference electrode inserted between the two electrodes to reduce noise and improve common-mode signal suppression.

[0048] In addition, a preamplifier circuit can be included to amplify and filter the electromyography (EMG) signal upon its input. The preamplifier circuit signal is transmitted to the microcontroller of the main control module via an analog-to-digital converter (ADC). The preamplifier includes an AD8327 instrumentation amplifier, which integrates a radio frequency interference (RFI) filter to significantly suppress environmental electromagnetic interference without affecting the input impedance and common-mode rejection ratio within the frequency range. The filtering circuit employs a dual-T active filter to effectively remove power frequency interference.

[0049] The microcontroller, as the core component of the electrical stimulation intervention knee joint brace, is used to process and calculate the collected data and output the corresponding electrical stimulation signals. The MEMS is connected to the microcontroller through the IIC interface. The electromyography system signals are transmitted to the preprocessor through the electrode wires, and the preprocessor transmits the processed signals to the microcontroller through the ADC.

[0050] In this embodiment, the microcontroller may include an sEMG signal anomaly judgment module, used to determine whether muscle contraction is abnormal during walking based on the collected sEMG signals. For example, the microcontroller may include a neural network model, which can judge whether the contraction of each muscle is abnormal during walking. First, information such as acceleration and posture angle changes obtained from the MEMS accelerometer is used to calculate whether the corresponding lower limb is in the swing phase or support phase of the gait cycle, and this information is transmitted to the microcontroller via the HC interface. Simultaneously, surface electromyography electrodes placed on the major muscles near the knee joint collect the raw sEMG signals of muscle contraction changes around the knee joint during walking; the sEMG signals are amplified and filtered by a preamplifier circuit and then input to the microcontroller via an analog-to-digital converter (ADC). The sEMG signals of muscle contraction changes around the knee joint during a walking cycle are sorted from bottom to top and from front to back according to muscle location. A time-series signal matrix is ​​generated based on the sorting result and the sEMG signals at the cycle time points. The time-series signal matrix is ​​then expanded according to the temporal correlation of muscle movement to obtain an expanded matrix, which meets the requirement of uniform extraction of temporal features by the first convolutional unit. Feature extraction is performed on the expanded matrix using the first convolutional unit to obtain a first feature matrix. The first feature matrix is ​​then temporally correlated and expanded to obtain a temporally correlated expanded matrix. The temporal feature sequence is then input into a trained fully connected layer neural network model, and the probabilities of normal and abnormal muscle contraction are calculated based on the output of the fully connected layer neural network model. Abnormal muscle contraction includes: corresponding muscle underactivation, insufficient muscle contraction, abnormal contraction timing, and abnormal overall contraction pattern. Through the above method, the probability of normal and abnormal muscle contraction for each muscle around the knee can be calculated, and the need for electrical stimulation of the muscle can be determined based on the probability.

[0051] After determining that electrical stimulation is necessary, the acquired time series and the standard time series can be standardized by using the mean and variance of the time series acquired from each electromyographic electrode channel and the corresponding standard time series. Specifically, this standardization can be achieved using the following formula:

[0052]

[0053] Where, L(n)i For each channel, the time series data is collected, R(n). i For each channel, the standard time series is defined by μ[L(n)i] and μ[R(n)i]. i σ[L(n)i] and σ[R(n)i] are the mean values ​​of the time series and the standardized time series collected from each channel, respectively. i [ ] represents the variance of the time series and the standardized time series collected for each channel, respectively. The standardized time series is determined by comprehensively analyzing data collected from normal individuals during walking. Since muscle strength varies between patients and normal individuals, standardization is necessary. By using variance and mean, we can utilize the average value while ensuring that some characteristic values ​​are preserved for later calculations.

[0054] After standardizing the time series acquired from each electromyography (EMG) electrode channel and the corresponding standard time series, the cross-correlation function between them can be calculated. The cross-correlation function measures the similarity between two time series and their values ​​at two different times. Specifically, the cross-correlation function is as follows:

[0055]

[0056] Furthermore, the cross-correlation function can be converted into a continuous integral form function, and the maximum value of the continuous integral form function can be calculated. The continuous integral form function is as follows:

[0057]

[0058] r(t) i , l(t) i Let y(τ) be the continuous signal before sampling, and τ be the timing difference between the measured time signal and the standard time signal. Amplitude stabilization is applied to y(τ), and the following values ​​are obtained: Find the zero point of y(τ) and the value of τ corresponding to the maximum value of y(τ), where τ represents the time difference between the measured time signal and the standard time signal. This yields the time point and duration of stimulus application. Therefore, the time point at which electrical stimulation is applied in the sequence can be determined based on the τ value, and the duration of the applied electrical stimulation can be determined based on the difference between the two values.

[0059] In addition, the electrical stimulation intervention knee joint brace may also include: an approximation calculation module, used to establish feature vectors for each element of the time series collected within a preset time range and the corresponding element in the normally calibrated time series based on duration and intensity, and calculate the cosine approximation of each element and the corresponding element; and a ratio determination module, used to compare the calculated cosine approximation with a preset approximation threshold, and when it is less than the approximation threshold, determine the ratio of electrical stimulation based on the ratio of the difference between the cosine approximation and the approximation threshold to the approximation threshold.

[0060] The proportion of electrical stimulation applied, i.e., the intensity of electrical stimulation, varies depending on the specific type of muscle contraction abnormality. Therefore, in this embodiment, the proportion of electrical stimulation can be calculated using an approximation calculation module and a proportion determination module.

[0061] For example, based on known time-series information, the amplitude-standardized feature vectors of one hundred extracted data points are compared with the feature vectors of the normally calibrated data points. Standardization can be achieved by standardizing each element of the time series collected within a preset time range and the corresponding element in the normally calibrated time series, and then creating a feature vector for each standardized element. The similarity between the two is obtained using the cosine similarity calculation method, as follows: For two two-dimensional sample points a(x11,x12) and b(x21,x22), a concept similar to the cosine of the included angle is used to measure their similarity.

[0062]

[0063] Right now:

[0064]

[0065] Normalize each data point obtained from each channel and set it as a two-dimensional feature vector X. i =[i, X i i ] T Compared with the normally calibrated two-dimensional feature vector X s =[s,X s s ] TSubstituting into the above formula, the range of the cosine of the angle is [-1, 1]. A larger cosine indicates a smaller angle between the two vectors, and a smaller cosine indicates a larger angle. In this embodiment, a similarity greater than 0.85 can be set as normal. The greater the difference, the greater the external electrical stimulation is required, and the proportion of electrical stimulation is assigned according to the difference in similarity. The degree of difference in contraction timing, amplitude, etc., from the normal contraction pattern can be determined based on the cross-correlation function and the cosine function of the angle. When the microcontroller determines that there is a difference in the contraction pattern, it sends electrical stimulation switch and strength control commands to the electrical stimulation system. After receiving the signals for the start and stop of electrical stimulation and the intensity of stimulation, the electrical stimulator performs low-frequency electrical stimulation intervention on the corresponding muscles, activating the contraction of the corresponding muscles and making the overall muscle contraction pattern closer to the normal pattern.

[0066] After determining the timing, duration, and intensity of the stimulation, the microcontroller converts this information into a corresponding electrical stimulation signal and sends it to the electrical stimulator. The electrical stimulator is a multi-channel neuromuscular electrical stimulator with a current frequency range of 0–100 Hz and a current intensity of 0–100 mA. It primarily releases low-frequency currents with a frequency of 10–50 Hz, a pulse width of 200–400 μs, and adjustable amplitude. The specific connection method is as follows: the microcontroller is connected to the electrical stimulator, and the stimulator is connected to the corresponding electrical stimulation electrode pads. In this embodiment, the electrical stimulation electrode pads can be attached to the same fixed position as or near the electromyographic electrodes to ensure more accurate muscle stimulation.

[0067] The electrical stimulation intervention knee brace provided in this embodiment includes an sEMG signal anomaly judgment module, used to determine whether muscle contraction is abnormal during walking based on the acquired sEMG signals; a standardization processing module, used to standardize the acquired time series and standard time series based on the mean and variance of the time series acquired by each electromyography electrode channel and the corresponding standard time series when abnormal muscle contraction is judged, to obtain a standardized acquisition time series and a standardized standard time series; a cross-correlation function calculation module, used to calculate the cross-correlation function of the standardized acquisition time series and the standardized standard time series; a conversion module, used to convert the cross-correlation function into a continuous integral form function and calculate the maximum value of the continuous integral form function; and a determination module, used to determine the time difference between the measured time signal and the standard time signal corresponding to the maximum value, and to determine the time point and duration of stimulation based on the time interpolation. Multiple intelligent sensors can monitor lower limb kinematic parameters and neuroelectromyographic signals. Machine learning models can be used to determine the activation level, contraction timing, and contraction intensity of muscles around the knee joint. In cases of abnormality, the model can automatically identify the differences and release corresponding low-frequency current stimulation to correct the abnormal contraction patterns of muscles around the knee joint. This can enhance the strength of the patient's lower limb muscles, improve muscle contraction patterns, and prevent muscle atrophy.

[0068] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A knee joint brace for electrical stimulation intervention, characterized in that, include: The sEMG signal abnormality judgment module is used to determine whether muscle contraction is abnormal during walking based on the collected surface electromyography signals. The standardization module is used to standardize the acquired time series and the standard time series when judging abnormal muscle contraction, based on the mean and variance of the time series acquired by each electromyographic electrode channel and the corresponding standard time series, to obtain a standardized acquired time series and a standardized standard time series. The standardization process is implemented in the following way: Where, L(n) i For each channel, the time series data is collected, R(n). i For each channel, the standard time series is defined by μ[L(n)i] and μ[R(n)i]. i σ[L(n)i] and σ[R(n)i] are the mean values ​​of the time series and the standardized time series collected from each channel, respectively. i [ ] represents the variance of the time series acquired by each channel and the standardized standard time series, respectively; The cross-correlation function calculation module is used to calculate the cross-correlation function between the standardized acquired time series and the standardized standard time series. The cross-correlation function is as follows: The conversion module is used to convert the cross-correlation function into a continuous integral form function and calculate the maximum value of the continuous integral form function, which is as follows: r(t) i , l(t) i The signal is a continuous signal before sampling, and τ is the timing difference between the time signal to be measured and the standard time signal. The determination module is used to determine the timing difference between the test time signal corresponding to the maximum value and the standard time signal, and to determine the time point and duration of the stimulus application based on the timing difference.

2. The electrical stimulation intervention knee joint brace according to claim 1, characterized in that, The electrical stimulation knee brace also includes: The approximation calculation module is used to establish feature vectors for each element of the time series collected within a preset time range and the corresponding element in the normally calibrated time series based on duration and intensity, and to calculate the cosine approximation of each element and the corresponding element. The ratio determination module is used to compare the calculated cosine approximation with a preset approximation threshold. When the cosine approximation is less than the approximation threshold, the ratio of electrical stimulation is determined based on the ratio of the difference between the cosine approximation and the approximation threshold to the approximation threshold.

3. The electrical stimulation intervention knee joint brace according to claim 2, characterized in that, The approximation calculation module includes: The standardization processing unit is used to standardize each element of the time series collected within a preset time range and the corresponding element in the normally calibrated time series, and to establish a feature vector for each standardized element to facilitate the calculation of the cosine approximation.

4. The electrical stimulation intervention knee joint brace according to claim 1, characterized in that, The electrical stimulation knee brace also includes: A gait information measuring device, wherein the gait information measuring unit is used to measure the user's gait information to determine a stride period; Correspondingly, the sEMG signal anomaly judgment module is used to determine whether muscle contraction is abnormal during walking based on the sEMG signals collected within a stride period.

5. The electrical stimulation intervention knee joint brace according to claim 4, characterized in that, The gait information measuring device includes: A microelectromechanical accelerometer is installed 10 cm above and below the patella on the anterolateral aspect of the thigh.

6. The electrical stimulation intervention knee joint brace according to claim 1, characterized in that, The electrical stimulation knee brace also includes: An electrostimulation device is used to perform low-frequency electrical stimulation on corresponding muscles using electrode patches according to the time point and duration of stimulation, thereby activating muscle contraction in the corresponding area. The electrode patches are respectively placed on the vastus medialis, rectus femoris, vastus lateralis, tibialis anterior, biceps femoris, semitendinosus and semimembranosus, medial and lateral sides of the gastrocnemius, and are located in the same positions as the electromyographic electrodes.

7. The electrical stimulation intervention knee joint brace according to claim 1, characterized in that, The electrical stimulation intervention knee brace also includes: A knee brace, comprising: upper and lower movable arms, a fixing strap, and a joint hinge, wherein the joint hinge is movably mounted on the upper and lower movable arms, and the fixing strap is mounted on the upper and lower movable arms.

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