Real-Time Hand Gesture Recognition Using the Myo Armband and Muscle Activity Detection

Hand gesture recognition consists of identifying the class and the instant of occurrence of a given movement of the hand. The solutions to this problem have many applications in science and technology. In this paper, we propose a model for hand gesture recognition in real time. This model takes as input the surface electromyography (EMG) measured on the muscles of the forearm by the Myo armband. For any user, the proposed model can learn to recognize any gesture of the hand through a training process. As part of this process a user needs to record 5 times, during 2 s each, the EMG on his forearm, close to the elbow, while performing the gesture to recognize. The k-nearest neighbor and the dynamic time warping algorithms are used for classifying the EMGs seen through a window. As part of the proposed model, we also include a detector of muscle activity that speeds the time of processing up and improves the accuracy of the recognition. We tested the proposed model at recognizing the 5 gestures defined by the proprietary recognition system of the Myo armband, achieving an accuracy of 89.5%. Finally, we also demonstrated that the model proposed in this work outperforms other systems, including the recognition system of the Myo.