Evaluation of Surface Electromyogram Signal Decomposition Methods in the Design of Hand Movement Recognition System

One method for determining motor commands to control hand prostheses is to use surface electr omy ogr am (sEMG) signal patterns. Due to the random and non-stationary nature of the signal, the idea of using signal information in small time intervals was investigated. In this study, with the aim of more accurate and faster detection of hand movements, two signal decomposition methods, namely discrete wavelet transform (DWT) and empirical mode decomposition (EMD) were evaluated. The sEMG sign als of the Ninapro-DB1 dataset, which were extracted from 27 healthy subjects while performing hand and finger movements, were used to design the system. Simple time domain features with fast calculation capability were extracted for each subband of the decomposed signals. Also, support vector machine (SVM) using different kernel functions was applied as a classifier. The results show that the use of DWT and EMD methods with the ability to access the information of time and frequency sub-intervals of the signals, provides better results in identifying hand movements compared to previous studies. With the EMD method and eight intrinsic mode functions (IMF), the highest recognition accuracy of 83.3% was obtained for six movements. Also, the DWT with the Bior5.5 mother wavelet and five levels of decomposition, achieved 80% recognition accuracy for ten movements and with the Coif2 mother wavelet and six levels of decomposition, the accuracy was 83.33% for eight movements. The results show the better performance of the DWT decomposition method compared to EMD for the design of the hand movement recognition system using sEMG signal patterns.