Short-term hand gestures recognition based on electromyography signals

Abstract

<p>Electromyography pattern recognition to predict limb movements can<br />significantly enhance the control of the prosthesis. However, this technique<br />has not yet been widely used in clinical practice. Improvements in the<br />myoelectric pattern recognition (MPR) system can improve the functionality<br />of the prosthesis. This study proposes new sets of time domain features to<br />enhance the MPR control system. Three groups of features are evaluated, time<br />domain 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.</p>