A Recurrent Neural Network for Hand Gesture Recognition based on Accelerometer Data

For many applications, hand gesture recognition systems that rely on biosignal data exclusively are mandatory. Usually, theses systems have to be affordable, reliable as well as mobile. The hand is moved due to muscle contractions that cause motions of the forearm skin. Theses motions can be captured with cheap and reliable accelerometers placed around the forearm. Since accelerometers can also be integrated into mobile systems easily, the possibility of a robust hand gesture recognition based on accelerometer signals is evaluated in this work. For this, a neural network architecture consisting of two different kinds of recurrent neural network (RNN) cells is proposed. Experiments on three databases reveal that this relatively small network outperforms by far state-of-the-art hand gesture recognition approaches that rely on multi-modal data. The combination of accelerometer data and an RNN forms a robust hand gesture classification system, i.e., the performance of the network does not vary a lot between subjects and it is outstanding for amputees. Furthermore, the proposed network uses only 5 ms short windows to classify the hand gestures. Consequently, this approach allows for a quick, and potentially delay-free hand gesture detection.