Research on Wavelet Packet Sample Entropy Features of sEMG Signal in Lower Limb Movement Recognition

In order to extract deeper features from surface electromyography signals and improve the classification accuracy of lower limb movements, a feature extraction method combining wavelet packet and sample entropy (WPT-SampEn) is proposed to accurately identify three types of lower limb movements. The electromyographic signals are preprocessed, which includes Butterworth filtering, activity segment detection based on short-term energy, and normalization processing. A three-layer wavelet packet decomposition method is used to decompose the five electromyographic signals into eight different frequency bands. By calculating the energy proportion in each frequency band, the top four frequency bands are determined as the focus of analysis. The Kruskal-Wallis test is employed to select frequency bands with statistical differences. To validate the effectiveness of this method, the support vector machine (SVM) algorithm is used for lower limb motion classification. Experimental results show that using the wavelet packet sample entropy features of the lateral thigh, medial thigh, rectus femoris, and biceps femoris muscles, the recognition rate reaches up to 96.46%. Compared with existing methods, this approach can extract deeper features from sEMG signals and achieve higher recognition accuracy. It has great potential in areas such as rehabilitation training, wearable exoskeleton control, and daily activity monitoring.