Wavelet Packet Local Energy Decomposition Histogram for Surface EMG Signals and Feature Energy Extraction

This article introduces the wavelet packet local energy decomposition method for extracting characteristic energy from surface electromyography (EMG) signals. EMG signals are electrical signals generated by human muscles during movement, which can be captured using electrodes. The acquired signals undergo wavelet packet decomposition, which breaks down the signal into wavelet packet coefficients across different frequency bands and time domains. Through local energy analysis, we obtain the local energy values within each wavelet packet component. These energy values are then visualized as histogram plots, enabling clear observation of the characteristic energy distribution patterns in the signals, thereby enhancing our understanding of EMG signal properties and features. From an implementation perspective, the process typically involves: 1) Preprocessing the raw EMG signals using filters to remove noise, 2) Applying wavelet packet decomposition algorithms (such as wpdec function in MATLAB) to decompose signals into multiple nodes, 3) Calculating energy values for each node using energy computation functions, 4) Sorting and selecting dominant energy nodes for feature extraction, and 5) Generating histogram visualizations using plotting functions like bar() in MATLAB. This methodology finds extensive applications in various fields including biomedical engineering, sports science, and rehabilitation medicine for muscle activity analysis and movement pattern recognition.