Real-Time Hand Gesture Recognition Using Temporal Muscle Activation Maps Of Multi-Channel Semg Signals

Accurate and real-time hand gesture recognition is highly beneficial for improving the control of advanced hand prosthesis. Surface Electromyography (sEMG) signals obtained from the forearm are widely used in this area. In this paper, we introduce a novel gesture representation called Temporal Muscle Activation (TMA) maps which captures information about the activation patterns of muscles in the forearm. Based on these maps, we propose an algorithm that can recognize hand gestures in real-time using a Convolution Neural Network. The algorithm was tested on 8 healthy subjects with sEMG signals acquired from 8 electrodes placed along the circumference of the forearm. The average classification accuracy of the proposed method was 94%, which is comparable to state-of-the-art methods. The average computation time of a prediction was 5.5ms, making the algorithm ideal for the real-time gesture recognition applications.