  Accurate and real-time hand gesture recognition is essential for controlling
advanced hand prostheses. Surface Electromyography (sEMG) signals obtained from
the forearm are widely used for this purpose. Here, we introduce a novel hand
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.
