Comparation Classification of EMG Signals in the Time Domain and Time-Frequency Domain

Electromyography (EMG) has been used extensively in motion recognition for rehabilitation or prosthetic control. One of the challenges in EMG-based motion recognition is the effective and accurate classification of EMG signals. In this study, we compare the performance of EMG signal classification between feature extraction in the time domain and time-frequency domain. EMG signal data was obtained from a public dataset with a total of 36 subjects. The feature extraction method in the time domain will use statistical features such as root mean square (RMS), mean absolute value (MAV), variance (VAR), zero crossing (ZC), and Integrated EMG (IEMG), while the method in the time-frequency domain will use the wavelet transform (WT). EMG signal classification will be carried out using several classifiers including Support vector machines, K-NN (k-Nearest Neighbor), Decision Tree (DT), and Random Forest (RF). The results showed that the method with feature extraction in the time-frequency domain was significantly better than the method in the time domain in classifying EMG signals using a random forest classifier with a higher classification accuracy ratio (99.95% vs. 95.50%). The results of this study can be used as a reference in selecting a more accurate and effective EMG signal classification method in motion recognition during rehabilitation or prosthetic control.