Regresar

Real-Time Hand Gesture Recognition: A Long Short-Term Memory Approach with Electromyography

Abstract:

Hand gestures are a non-verbal type of communication ideally suited for Human-Machine Interaction. Nevertheless, accuracy rates and response times still are a matter of research. One unattended problem has been the difficulty and vagueness of the evaluation of the models proposed in the literature. In this paper, a protocol for evaluating recognition is proposed. A Hand Gesture Recognition system using electromyography signals (EMG) is also presented. This model works in real time, is user dependent and is based in Long Short-Term Memory Networks. The model recognizes 5 different classes (wave in, wave out, fist, open, pinch) apart from the relax state. A data set with 120 people was collected using the commercial device Myo Armband. The data set was divided 50% for tuning and 50% for testing. Following the evaluation protocol proposed, the presented model achieves a 95.79% in classification and a 88.1% in recognition accuracy. An analysis of the characteristics of this model shows the advantage over similar models and its capability for being applied in all sort of fields.