Short-term hand gestures recognition based on electromyography signals - Raghad Radi Essa; Hanadi A. Jaber; Abbas A. Jasim (Institute of Advanced Engineering and Science, 2023)
Electromyography pattern recognition to predict limb movements cansignificantly enhance the control of the prosthesis. However, this techniquehas not yet been widely used in clinical practice. Improvements in themyoelectric pattern recognition (MPR) system can improve the functionalityof the prosthesis. This study proposes new sets of time domain features toenhance the MPR control system. Three groups of features are evaluated, timedomain with auto regression (TD-AR), frequency domain (FD), and timefrequency domain (TFD). The electromyography signals (EMG) are obtained from the Ninapro database-5 (DB5), a publicly available dataset for hand prosthetics. The long-term signals of DB5 are divided into short-term signals to perform short-term signals recognition. The three feature sets are extracted from the short-term signals. The results showed that the performance of the proposed TD-AR features outperformed that of the FD and TFD feature sets. The TD-AR-based discrimination performance of 40 gestures achieved a precision of 88.8% and a sensitivity of 82.6%. The integration of short-term identification with reliable features can improve classification accuracy even for a large number of gestures. A comparison with the latest works shows the reliability of the proposed work.