A transient phase electromyography signal gesture recognition method based on electromyography activation intensity transfer characteristics and adaptive analysis window length

Abstract

The application provides a transient phase electromyography signal gesture recognition method based on electromyography activation intensity transfer characteristics and adaptive analysis window length, aiming to solve the problems of imbalance between spatial characteristics and operation cost, imbalance between recognition accuracy and response speed, and low algorithm generalization between individuals and tasks when traditional electromyography signal processing technology is used in transient phase signals. The method first performs signal acquisition and preprocessing, starting point detection, signal frame formatting, feature extraction and SVM classifier training in offline mode; in online mode, the preprocessed signal is collected, the starting point is detected, the signal is formatted, the electromyography activation intensity transfer characteristics are extracted, the offline SVM classifier is input, the analysis window length is dynamically adjusted according to the confidence, and finally the confidence threshold is adjusted in real time. It is verified by practice that the method can effectively balance the spatial characteristics and operation cost, balance the recognition accuracy and response speed in different individuals and tasks, significantly improve the generalization of the algorithm, and provide more efficient and accurate technical support for the field of prosthetic control, motion rehabilitation equipment and the like.

Description

Technical Field

[0001] This invention relates to the field of biosignal processing and prediction technology, particularly to a technique for predicting motor intentions using the transient phase of electromyographic signals, which is widely used in fields such as prosthetic control and sports rehabilitation equipment. Background Technology

[0002] Electromyography (EMG) signals are bioelectrical signals generated on the skin surface during muscle contraction. They contain rich information about muscle activity and can be used to predict motor intent. Traditional EMG signal processing methods typically rely on steady-state signals for feature extraction, but transient signals (i.e., the transition from rest to motion) contain important information about motor intent, which is particularly important for improving the speed of motor intent recognition. However, existing technologies utilizing transient signals are based on fixed-length analysis windows, which has the following problems:

[0003] The imbalance between spatial features and computational cost: Existing analysis methods either rely on analyzing the conventional time-domain features of discrete channel signals that have lost their spatial characteristics, or use computationally expensive deep learning methods such as CNN and LSTM models. Therefore, it is difficult to achieve both spatial features and low computational cost.

[0004] Imbalance between recognition accuracy and response speed: Increasing the analysis window length improves recognition accuracy but reduces response speed; conversely, shortening the analysis window length improves response speed but reduces recognition accuracy. Therefore, a fixed analysis window length cannot balance recognition accuracy and response speed.

[0005] Low algorithmic generalization between individuals and tasks: Existing electromyography signal processing methods are usually based on a fixed analysis window length, which cannot adapt to changes in different individuals and different tasks.

[0006] This invention proposes a gesture recognition method that can dynamically adjust the analysis window length, which can adaptively adjust the analysis window length for different individuals under different tasks, and achieve a balance between recognition accuracy and response speed by evaluating the confidence level. Summary of the Invention

[0007] This invention proposes a transient phase electromyographic signal gesture recognition method based on electromyographic activation intensity transfer characteristics and adaptive analysis window length. It can adaptively adjust the analysis window length for different individuals under different tasks, and achieve a balance between recognition accuracy and response speed by evaluating the confidence level.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows: a method for gesture recognition of transient phase electromyographic signals based on electromyographic activation intensity transfer characteristics and adaptive analysis window length, the method comprising the following steps:

[0009] S1: Signal acquisition and preprocessing in offline mode: Offline acquisition and preprocessing of electromyographic signals are performed using a high-density electromyography sensor.

[0010] S2: Starting point detection in offline mode: Using a starting point T0 detection algorithm based on detEMG, the starting moment of the motion intention is identified.

[0011] S3: Signal frame formatting in offline mode: Frame formatting is performed on the signal throughout the entire transient phase after the starting point T0.

[0012] S4: Feature extraction in offline mode: For each frame of signal, calculate the electromyographic activation intensity transfer (directional activation intensity transfer statistics) features sequentially.

[0013] S5: Training SVM classifiers: This stage is equivalent to training multiple classifiers corresponding to different window lengths.

[0014] S6: Signal acquisition and preprocessing in online mode: Online signal acquisition and preprocessing is performed using a high-density electromyography sensor.

[0015] S7: Signal start point detection in online mode: using mEMG-based detection onset Online signal start point T0 online The detection algorithm identifies the start time of the motion intent.

[0016] S8: Signal frame formatting in online mode: from start point T0 online The signals during the entire transient phase thereafter undergo frame formatting.

[0017] S9: Feature extraction in online mode: For each frame of signal, calculate the electromyographic activation intensity transfer (directional activation intensity transfer statistics) features sequentially.

[0018] S10: Input offline SVM classifier: Calculate the prediction type and confidence level of the current frame and perform dynamic window length adjustment.

[0019] S11: Real-time feedback and adjustment: Adjust the confidence threshold based on real-time results.

[0020] Furthermore, the high-density electromyography sensor described in S1 needs to be able to provide 64 or more channels of electromyography signals in order to acquire electrical activity from different muscle regions.

[0021] Furthermore, the signal preprocessing method described in S1 adopts the following steps: S1-1: 50Hz notch filtering: remove power frequency interference.

[0022] S1-2: Bandpass filter (30Hz-500Hz): Simulates muscle characteristics, removes low-frequency and high-frequency noise, and retains effective electromyographic signals.

[0023] Furthermore, in S2, the start point detection in offline mode uses mEMG-based methods. onset Starting point T0 detection algorithm. This algorithm calculates detEMG to find the starting point of signal abrupt change, thus identifying the start of motion intent. The specific steps are as follows:

[0024] S2-1: First, sum the data from all channels, which is equivalent to compressing the high-density electromyography signal into one channel. Based on this, calculate the detEMG using the following formula:

[0025]

[0026] Where N is the total number of data points, |x i | is the absolute value of the i-th data point, mEMG i T is the electromyographic activation intensity value of the i-th data point. det It detects the time span, detEMG i It is the detection signal of the i-th data point.

[0027] S2-2: Calculate the moment when the value reaches 0.5 times the maximum value; this moment is the starting point T0.

[0028] S2-3: Simultaneously obtain the mEMG at this time. i And denoted as mEMG onset This value will be used for online identification later.

[0029] Furthermore, the method for formatting the signal frames in the entire transient phase in S3 is as follows: starting from T0, at fixed time intervals ΔT, the portion of the signal from each channel between time t(i-1) and time t(i) is averaged, and the result is defined as the signal of the i-th frame (i = 1, 2, 3... f_sum). f_sum is the total number of frames in the entire transient phase. The specific calculation formula is as follows:

[0030]

[0031] Among them, F i This is the signal of the i-th frame (multi-channel data; if the sensor size is 8*8, then it is in 8*8 matrix format), t i =T0+i*Δt is the end time of the i-th frame.

[0032] f_sum represents the total number of frames in the entire transient phase, and is calculated using the following formula:

[0033]

[0034] Where Δt is the time interval, T transient This is the duration of the entire transient phase, consistent with offline mode.

[0035] Furthermore, in S4, the feature extraction method in offline mode involves calculating electromyographic activation intensity transfer (directional activation intensity transfer statistics) features for each frame of signal. Electromyographic activation intensity transfer features can extract texture information of the signal within local regions, making them suitable for describing the spatial characteristics of electromyographic signals. These features have a strong ability to capture changes in muscle activity patterns. The specific calculation steps are as follows:

[0036] S4-1: Calculation of electromyographic activation intensity transfer characteristics

[0037] First, for each electromyography sensor channel in the current frame, calculate its ST in the horizontal direction. x and vertical direction ST y Electromyographic intensity transfer values:

[0038] ST x =mEMG(x+1,y)-mEMG(x-1,y)

[0039] ST y =mEMG(x,y+1)-mEMG(x,y-1)

[0040] Where mEMG(x,y) is the electromyographic activation intensity value at the electromyographic sensor channel (x,y) in the current frame.

[0041] Then, the activation intensity transfer amplitude ST and activation intensity transfer direction θ for each electromyography sensor channel (x,y) are calculated:

[0042]

[0043] S4-2: Constructing a statistical graph of electromyographic activation intensity transfer

[0044] The current frame is divided into several subframes, each with 2x2 channels. Within each subframe, an activation intensity transition graph is calculated based on the activation intensity transition direction of each channel. The specific steps are as follows:

[0045] S4-2-1 divides the activation intensity transfer direction θ (0-180°) into several intervals.

[0046] S4-2-2 assigns the activation intensity transfer amplitude ST of each electromyography (EMG) sensor channel to the corresponding statistical graph slot according to its activation intensity transfer direction θ. Specifically, if the activation intensity transfer direction θ of the EMG sensor channel falls in the k-th interval, then the activation intensity transfer amplitude ST of that EMG sensor channel is added to the statistical graph of the k-th interval.

[0047] S4-2-3 converts the statistical graph into a feature vector, resulting in the final electromyographic activation intensity transfer feature vector.

[0048] Furthermore, training an SVM classifier in S5 involves the following steps:

[0049] S5-1: Divide the training set and the test set

[0050] All gesture sample data are divided into training and test sets according to a certain ratio, which can be selected between 1:4 and 1:2.

[0051] S5-2: Signal Input

[0052] Starting from frame m (the initial input frame), the electromyographic activation intensity transfer features of the signals from frame m to frame m+1 are averaged across dimensions and then input into the SVM classifier.

[0053] S5-3: Prediction results and confidence calculation. For each gesture category, the SVM classifier outputs a prediction result and its confidence value. The confidence value represents the reliability of the prediction result.

[0054] S5-4: Adjust the signal input, continuously extend the signal window length range of the input SVM classifier, execute 5-3 and 5-4 until all frames in the transient phase are covered.

[0055] Furthermore, the signal preprocessing method described in S6 employs the following steps:

[0056] S6-1: 50Hz notch filter: Removes power frequency interference.

[0057] S6-2: Bandpass filter (30Hz-500Hz): Simulates muscle characteristics, removes low-frequency and high-frequency noise, and retains effective electromyographic signals.

[0058] Furthermore, the EMG-based method described in S7 onset Online signal start point T0 online The detection algorithm employs the following steps:

[0059] S7-1: First, sum the data from all channels, which is equivalent to compressing the high-density electromyography signal into one channel. Based on this, calculate the mEMG using the following formula. i :

[0060]

[0061] Where N is the total number of data points, |x i | is the absolute value of the i-th data point, mEMG i It is the electromyographic activation intensity value of the i-th data point.

[0062] S7-2: Compare current mEMG i With mEMG onset (starting time mEMG) i (threshold), until the current mEMG i Greater than or equal to mEMG onset This point is now marked as the starting point T0. online .

[0063] Furthermore, the specific method for formatting the signal frame described in S8 is as follows: from T0 online Then, at fixed time intervals ΔT, the portion of the signal from each channel between time t(i-1) and time t(i) is averaged, and the result is defined as the signal of the i-th frame (i = 1, 2, 3... f_sum). f_sum is the total number of frames in the entire transient phase. Refer to S3 for the specific calculation formula.

[0064] Furthermore, in S9, the feature extraction method in online mode involves calculating electromyographic activation intensity transfer (directional activation intensity transfer statistics) features for each frame of signal. The specific calculation method and formula are detailed in S4.

[0065] Furthermore, the specific steps for inputting the offline SVM classifier in S10 are as follows:

[0066] S10-1: Calculate the prediction type and its confidence level for the current frame.

[0067] Starting from frame m (the initial input frame), the electromyographic activation intensity transfer features of the signals from frame m to frame m+1 are averaged dimensionally and input into the corresponding offline SVM classifier. The current prediction result (Result) and its confidence level (Confidence) are then calculated.

[0068] S10-2: Dynamic window length adjustment. Adjust the confidence level (Confidence) from the previous step to the set confidence threshold (Confidence). accept The comparison is performed. If the confidence level reaches the set acceptable confidence level, the current prediction result is output and further data acquisition stops. If not, the length of the input signal analysis window is increased, and step 10-1 is executed again until the confidence level reaches the set acceptable confidence level. S10-3: Output the result, output the recognition result, and stop further data acquisition. If the length of the input signal has covered the entire transient phase, the recognition result is also output.

[0069] Furthermore, the specific methods for real-time feedback and adjustment in S11 are as follows:

[0070] S11-1: Preset initial confidence threshold accept_0

[0071] S11-2: Adjust the confidence threshold based on real-time results, adapting to the needs of different individuals and tasks. acceptt_new This allows for dynamic adjustment of signal acquisition and analysis strategies, achieving a balance between prediction accuracy and response speed.

[0072] Compared with the prior art, the advantages of the present invention are as follows:

[0073] 1. Reduced computational cost: Existing analysis methods, while preserving spatial features, can only utilize costly deep learning methods such as CNN and LSTM models. This solution, using electromyography activation intensity transfer features, can significantly reduce computational costs.

[0074] 2. Improved response speed: Compared with other gesture recognition solutions based on electromyography signals, this solution directly processes the transitional signal before the signal stabilizes, which can advance the response time by about 400ms.

[0075] 3. Achieving a balance between recognition accuracy and response speed: This invention proposes a gesture recognition method that can dynamically adjust the analysis window length, which can adaptively adjust the analysis window length for different individuals under different tasks, and achieve a balance between recognition accuracy and response speed by evaluating the confidence level. Attached Figure Description

[0076] Figure 1 This is a schematic diagram of the method flow described in this invention.

[0077] Figure 2 This is a schematic diagram of the starting point detection results in an embodiment of this application.

[0078] Figure 3 This is a schematic diagram illustrating how the analysis window length and average accuracy change with the confidence threshold setting in the embodiments of this application. Detailed Implementation

[0079] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0080] Example: See Figures 1-3 A method for gesture recognition of transient phase electromyographic signals based on electromyographic activation intensity transfer features and adaptive analysis window length, the method comprising the following steps:

[0081] S1: Signal acquisition and preprocessing in offline mode. Gesture classification data from an open-source dataset was used, which employed a 128-channel high-density electromyography (EMG) sensor to acquire electrical activity from different muscle regions.

[0082] S1-1: 50Hz notch filter: Removes power frequency interference.

[0083] S1-2: Bandpass filter (30Hz-500Hz): Simulates muscle characteristics, removes low-frequency and high-frequency noise, and retains effective electromyographic signals.

[0084] S2: Starting point detection in offline mode: Using a starting point T0 detection algorithm based on detEMG, the starting moment of the motion intention is identified.

[0085] In S2's offline mode, the start point detection uses a detEMG-based start point T0 detection algorithm. This algorithm calculates the detEMG signal to find the start point of signal abrupt change, identifying the beginning of motion intent. The specific steps are as follows:

[0086] S2-1: First, sum the data from all channels, which is equivalent to compressing the high-density electromyography signal into one channel. Based on this, calculate the detEMG using the following formula:

[0087]

[0088] Where N is the total number of data points, |x i | is the absolute value of the i-th data point, mEMG i T is the electromyographic activation intensity value of the i-th data point. det It detects the time span, detEMG i It is the detection signal of the i-th data point.

[0089] S2-2: Calculate the moment when the value reaches 0.5 times the maximum value; this moment is the starting point T0.

[0090] S2-3: Simultaneously obtain the mEMG at this time. i And denoted as mEMG onset This value will be used for online identification later.

[0091] S3: Signal Frame Formatting in Offline Mode: The signal is formatted into frames throughout the entire transient phase starting from point T0. After T0, at fixed time intervals ΔT, the portion of the signal from each channel between time t(i-1) and time t(i) is averaged. The result is defined as the i-th frame signal (i = 1, 2, 3... f_sum), where f_sum is the total number of frames in the entire transient phase. The specific calculation formula is as follows:

[0092]

[0093] Among them, F i This is the signal of the i-th frame (multi-channel data; if the sensor size is 8*8, then it is in 8*8 matrix format), t i=T0+i*Δt is the end time of the i-th frame.

[0094] f_sum represents the total number of frames in the entire transient phase, and is calculated using the following formula:

[0095]

[0096] Where Δt is the time interval, which can be set to 50-100ms depending on the task, and T transient It is the duration of the entire transient phase, which can be set to an integer multiple of Δt between 300ms and 500ms, depending on the task.

[0097] S4: Feature extraction in offline mode: For each frame of signal, calculate the electromyographic activation intensity transfer (directional activation intensity transfer statistics) features sequentially. The specific calculation steps are as follows:

[0098] S4-1: Calculation of electromyographic activation intensity transfer characteristics

[0099] First, for each electromyography sensor channel in the current frame, calculate its ST in the horizontal direction. x and vertical direction ST y Electromyographic intensity transfer values:

[0100] ST x =mEMG(x+1,y)-mEMG(x-1,y)

[0101] ST y =mEMG(x,y+1)-mEMG(x,y-1)

[0102] Where mEMG(x,y) is the electromyographic activation intensity value at the electromyographic sensor channel (x,y) in the current frame.

[0103] Then, the activation intensity transfer amplitude ST and activation intensity transfer direction θ for each electromyography sensor channel (x,y) are calculated:

[0104]

[0105] S4-2: Constructing a statistical graph of electromyographic activation intensity transfer

[0106] The current frame is divided into several subframes, each with 2x2 channels. Within each subframe, an activation intensity transition graph is calculated based on the activation intensity transition direction of each channel. The specific steps are as follows:

[0107] S4-2-1 divides the activation intensity transfer direction θ (0-180°) into several intervals.

[0108] S4-2-2 assigns the activation intensity transfer amplitude ST of each electromyography (EMG) sensor channel to the corresponding statistical graph slot according to its activation intensity transfer direction θ. Specifically, if the activation intensity transfer direction θ of the EMG sensor channel falls in the k-th interval, then the activation intensity transfer amplitude ST of that EMG sensor channel is added to the statistical graph of the k-th interval.

[0109] S4-2-3 converts the statistical graph into a feature vector, resulting in the final electromyographic activation intensity transfer feature vector.

[0110] S5: Training SVM classifiers: This stage trains multiple classifiers corresponding to different window lengths.

[0111] S5-1: Divide the training and test sets. Divide all gesture sample data into training and test sets according to a certain ratio, which can be selected between 1:4 and 1:2.

[0112] S5-2: Signal Input

[0113] Starting from frame m (the initial input frame), the electromyographic activation intensity transfer features of the signals from frame m to frame m+1 are averaged across dimensions and then input into the SVM classifier.

[0114] S5-3: Prediction Results and Confidence Calculation

[0115] For each gesture category, the SVM classifier outputs a prediction result and its confidence value, where the confidence value indicates the reliability of the prediction result.

[0116] S5-4: Adjust signal input

[0117] The signal window length range input to the SVM classifier is continuously extended, and steps 5-3 and 5-4 are performed until all frames within the transient phase are covered.

[0118] S6: Signal acquisition and preprocessing in online mode: Online signal acquisition and preprocessing is performed using a high-density electromyography sensor.

[0119] S6-1: 50Hz notch filter: Removes power frequency interference.

[0120] S6-2: Bandpass filter (30Hz-500Hz): Simulates muscle characteristics, removes low-frequency and high-frequency noise, and retains effective electromyographic signals.

[0121] S7: Signal start point detection in online mode: using mEMG-based detection onset Online signal start point T0 online The detection algorithm identifies the start time of the motion intent.

[0122] S7-1: First, sum the data from all channels, which is equivalent to compressing the high-density electromyography signal into one channel. Based on this, calculate the mEMG using the following formula. i :

[0123]

[0124] Where N is the total number of data points, |x i | is the absolute value of the i-th data point, mEMG i It is the electromyographic activation intensity value of the i-th data point.

[0125] S7-2: Compare current mEMG i With mEMG onset (starting time mEMG) i (threshold), until the current mEMG i Greater than or equal to mEMG onset This point is now marked as the starting point T0. online .

[0126] S8: Signal frame formatting in online mode: from start point T0 online The signals during the entire transient phase thereafter undergo frame formatting. From T0 online Then, at fixed time intervals ΔT, the portion of the signal from each channel between time t(i-1) and time t(i) is averaged, and the result is defined as the signal of the i-th frame (i = 1, 2, 3... f_sum). f_sum is the total number of frames in the entire transient phase. Refer to S3 for the specific calculation formula.

[0127] S9: Feature extraction in online mode: For each frame of signal, calculate the electromyographic activation intensity transfer (directional activation intensity transfer statistics) features sequentially. Refer to S4 for specific calculation methods and formulas.

[0128] S10: Input offline SVM classifier: Calculate the prediction type and confidence level of the current frame and perform dynamic window length adjustment.

[0129] S10-1: Calculate the prediction type and its confidence level for the current frame.

[0130] Starting from frame m (the initial input frame), the electromyographic activation intensity transfer features of the signals from frame m to frame m+1 are averaged dimensionally and input into the corresponding offline SVM classifier. The current prediction result (Result) and its confidence level (Confidence) are then calculated.

[0131] S10-2: Dynamic window length adjustment.

[0132] Compare the confidence level (Confidence) from the previous step with the set confidence threshold (Confidence). accept The comparison is performed. If the confidence level reaches the set acceptable confidence level, the current prediction result is output and further data acquisition stops. If not, the input signal analysis window length is increased, and step 10-1 is executed again until the confidence level reaches the set acceptable confidence level.

[0133] S10-3: Output Results

[0134] Output the recognition result and stop collecting data. If the length of the input signal has covered the entire transient phase, also output the recognition result.

[0135] S11: Real-time feedback and adjustment: Adjust the confidence threshold based on real-time results.

[0136] S11-1: Preset initial confidence threshold accept_0

[0137] S11-2: Adjust the confidence threshold based on real-time results, adapting to the needs of different individuals and tasks. accept_new This allows for dynamic adjustment of signal acquisition and analysis strategies, achieving a balance between prediction accuracy and response speed.

[0138] like Figure 3 The figure shows how the analysis window length and average accuracy change with the confidence threshold setting in the embodiments of this application.

[0139] It should be noted that the above embodiments are not intended to limit the scope of protection of the present invention. Equivalent transformations or substitutions made based on the above technical solutions all fall within the scope of protection of the claims of the present invention.

Claims

1. A method for gesture recognition of transient phase electromyographic signals based on electromyographic activation intensity transfer features and adaptive analysis window length, characterized in that, The method includes the following steps: S1: Offline mode signal acquisition and preprocessing, using a high-density electromyography (EMG) sensor for offline acquisition and preprocessing of EMG signals. S2: Starting point detection in offline mode, using a method based on... The starting point T0 detection algorithm identifies the start time of the motion intention. S3: Signal frame formatting in offline mode, performing frame formatting processing on the signal throughout the entire transient phase after the start point T0. S4: Feature extraction in offline mode. For each frame of signal, the electromyographic activation intensity transfer features are calculated sequentially. S5: Train the SVM classifier. This stage is equivalent to training multiple classifiers corresponding to different window lengths. S6: Online signal acquisition and preprocessing, using high-density electromyography sensors for online signal acquisition and preprocessing. S7: Signal start-point detection in online mode, using a based... Online signal start point The detection algorithm identifies the start time of the motion intent. S8: Signal frame formatting in online mode, from the start point The signal during the entire transient phase thereafter undergoes frame formatting processing. S9: Feature extraction in online mode. For each frame of signal, the electromyographic activation intensity transfer features are calculated sequentially. S10: Input the offline SVM classifier, calculate the predicted type and confidence level of the current frame, and dynamically adjust the window length. S11: Real-time feedback and adjustment, adjusting the confidence threshold based on real-time results; In S4, the offline mode feature extraction method involves calculating electromyographic activation intensity transfer features for each frame of signal. These features can extract texture information of the signal within a local region, making them suitable for describing the spatial features of electromyographic signals. The electromyographic activation intensity transfer features have a strong ability to capture changes in muscle activity patterns. The specific calculation steps are as follows: S4-1: Calculate the characteristics of electromyographic activation intensity transfer. First, for each electromyography (EMG) sensor channel in the current frame, calculate its horizontal direction. and vertical direction Electromyographic intensity transfer values: in, The current frame is in the electromyography sensor channel. The electromyographic activation intensity value at each location was then calculated, and then the value for each electromyographic sensor channel was determined. activation intensity transfer amplitude and activation intensity transfer direction : S4-2: Construct an electromyographic activation intensity transfer statistical map. Divide the current frame into several subframes, each with 2*2 channels. Within each subframe, calculate the activation intensity transfer statistical map based on the activation intensity transfer direction of each channel. The specific steps are as follows: S4-2-1 will shift the activation intensity direction Divided into several intervals, The range is 0-180°, and S4-2-2 transfers the activation intensity amplitude of each electromyography sensor channel. Based on its activation intensity transfer direction Assign them to the corresponding statistical chart slots. Specifically, if the activation intensity transfer direction of the electromyography sensor channel... If the value falls within the k-th interval, then the activation intensity shift amplitude of the electromyography sensor channel is... In the statistical graph added to the k-th interval, S4-2-3 converts the statistical graph into a feature vector, resulting in the final electromyographic activation intensity transfer feature vector.

2. The method for gesture recognition of transient phase electromyographic signals based on electromyographic activation intensity transfer features and adaptive analysis window length according to claim 1, characterized in that, In S1, the high-density electromyography sensor needs to be able to provide 64 or more channels of electromyography signals in order to acquire electrical activity from different muscle regions; The signal preprocessing method in S1 adopts the following steps: S1-1: 50Hz notch filter: removes power frequency interference. S1-2: Bandpass filter 30Hz-500Hz: Simulates muscle characteristics, removes low-frequency and high-frequency noise, and retains effective electromyographic signals; In step S2, the starting point detection in offline mode uses a method based on... The starting point T0 detection algorithm calculates the signal's... This allows us to pinpoint the starting point of the signal mutation and identify the beginning of the intended movement, using the following specific steps: S2-1: First, sum the data from all channels, which is equivalent to compressing the high-density electromyography signal into one channel. Based on this, calculate according to the following formula. : in, It is the total number of data points. It is the first The absolute value of each data point. It is the first Electromyographic activation intensity values ​​for each data point It detects the span of time. It is the first The detection signal of each data point S2-2: Calculate the moment when the value reaches 0.5 times the maximum value; this moment is the starting point T0. S2-3: Simultaneously obtain the current state. , and record as This value will be used for online identification later.

3. The method for gesture recognition of transient phase electromyographic signals based on electromyographic activation intensity transfer features and adaptive analysis window length according to claim 1, characterized in that, The method for formatting the signal frames during the entire transient phase in S3 is as follows: Starting from T0, at fixed time intervals ∆T, the portion of the signal from each channel between time t(i-1) and time t(i) is averaged, and the result is defined as the i-th frame signal, i=1,2,3... , This refers to the total number of frames in the entire transient phase, calculated using the following formula: in, It is the signal of the i-th frame. It is the end time of the i-th frame. The total number of frames in the entire transient phase is represented by the following formula: in, This is a time interval, which can be set to 50-100ms depending on the task. This refers to the entire transient phase time, which can be set to between 300ms and 500ms depending on the task. Integer multiples of.

4. The method for gesture recognition of transient phase electromyographic signals based on electromyographic activation intensity transfer features and adaptive analysis window length according to claim 1, characterized in that, Training an SVM classifier in S5 involves the following steps: S5-1: Divide the data into training and testing sets. All gesture sample data are divided into training and testing sets according to a certain ratio, which can be chosen between 1:4 and 1:

2. S5-2: Signal input. Starting from frame m (the initial input frame), the electromyographic activation intensity transfer features of the signals from frame m to frame (m+1) are averaged across dimensions and input into the SVM classifier. S5-3: Prediction Results and Confidence Calculation. For each gesture category, the SVM classifier outputs a prediction result and its confidence value. The confidence value represents the reliability of the prediction result. S5-4: Adjust the signal input, continuously extend the signal window length range of the input SVM classifier, execute 5-3 and 5-4 until all frames in the transient phase are covered.

5. The method for gesture recognition of transient phase electromyographic signals based on electromyographic activation intensity transfer features and adaptive analysis window length according to claim 1, characterized in that, The signal preprocessing method in S6 adopts the following steps: S6-1: 50Hz notch filter to remove power frequency interference. S6-2: Bandpass filter 30Hz-500Hz, simulates muscle characteristics, removes low-frequency and high-frequency noise, and retains effective electromyographic signals; S7 based on Online signal start point The detection algorithm employs the following steps: S7-1: First, sum the data from all channels, which is equivalent to compressing the high-density electromyography signal into one channel. Based on this, calculate according to the following formula. : in, It is the total number of data points. It is the first The absolute value of each data point. It is the first Electromyographic activation intensity values ​​for each data point S7-2: Compare the current situation and That is, the start time Threshold, until the current Greater than or equal to The mark at this point is the starting point. .

6. The method for gesture recognition of transient phase electromyographic signals based on electromyographic activation intensity transfer features and adaptive analysis window length according to claim 1, characterized in that, The specific method for signal frame formatting in S8 is as follows: From Then, at fixed time intervals ∆T, the portion of the signal from each channel between time t(i-1) and time t(i) is averaged, and the result is defined as the signal of the i-th frame, i=1,2,3...f_sum, where f_sum is the total number of frames in the entire transient phase. It is a time interval. This is the duration of the entire transient phase, consistent with offline mode; In S9, the feature extraction method in online mode is to calculate the electromyographic activation intensity transfer feature for each frame of signal.

7. The method for transient phase electromyographic signal gesture recognition based on electromyographic activation intensity transfer features and adaptive analysis window length according to claim 1, characterized in that, The specific steps for inputting the offline SVM classifier in S10 are as follows: S10-1: Calculate the prediction type and its confidence level for the current frame. Starting from frame m, the initial input frame, the electromyographic activation intensity transfer features of the signals from frame m to frame (m+1) are averaged across dimensions and input into the corresponding offline SVM classifier to calculate the current prediction result. and its confidence level , S10-2: Dynamic window length adjustment The confidence level in the previous step With the set confidence threshold The comparison is performed. If the confidence level reaches the set acceptable confidence level, the current prediction result is output and further data collection stops. If not, the input signal analysis window length is increased, and step 10-1 is executed again until the confidence level reaches the set acceptable confidence level. S10-3: Output the recognition result and stop collecting data. If the length of the input signal has covered the entire transient phase, the recognition result will also be output.

8. The method for transient phase electromyographic signal gesture recognition based on electromyographic activation intensity transfer features and adaptive analysis window length according to claim 1, characterized in that, The specific methods for real-time feedback and adjustment in S11 are as follows: S11-1: Preset initial confidence threshold S11-2: Adjust the confidence threshold based on real-time results, adapting to the needs of different individuals and tasks. This allows for dynamic adjustment of signal acquisition and analysis strategies, achieving a balance between prediction accuracy and response speed.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the transient phase electromyographic signal gesture recognition method based on electromyographic activation intensity transfer features and adaptive analysis window length as described in any one of claims 1 to 8.

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