Muscle fatigue classification based on geometric features of sEMG signal
In order to better distinguish the degree of muscle fatigue,the energy changes of surface electromyography(sEMG)signals are analyzed in different frequency bands by the wavelet transform method,and the geometric features of the signals are extracted to distinguish the non-fatigue and fatigue states of the muscles. The features of perimeter,area,and roundness are extracted from the geometric boundary area,and geometric feature transformation is analyzed. At the same time,a classifier is used to classify muscle fatigue. The experimental results show that the geometric features have a more intuitive distinguishing effect on fatigue state of muscle. Geometric features have obvious changes before and after muscle fatigue. Compared with traditional time domain and frequency domain features,they have better classification effects. Feature fusion of geometric features can effectively improve the classification accuracy.
万方数据
维普
