Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier

Abstract Integration of surface EMG sensors as an input source for Human Machine Interfaces (HMIs) is getting an increasing attention due to their application in wearable devices such as armbands. For a wearable device, comfort and lightness are important factors. Therefore, in this article we focus on a minimalistic approach, in which we try to classify four gestures with only 2 EMG channels installed on the flexor and extensor muscles of the forearm. We adopted a two-channel EMG system, together with a high dimensional feature-space and a support vector machine (SVM) as a classifier. In addition, tolerance of the system for rejection of unsolicited gestures during the body movement was evaluated, and the two methods were implemented to ensure this; one based on an SVM threshold and another one based on the addition of a locking gesture. The resulting system is able to recognize up to 5 gestures (hand closing, hand opening, wrist flexion, wrist extension and double wrist flexion), presenting a classification accuracy of between 95% and 100% for a trained user and robustness against different body movements, guaranteed with the locking feature. We showed that misclassification of other gestures as the unlocking never happened for expert users.