Segmentation of electromyography signals for pattern recognition

Abstract

The use of gestures as interface between humans and robots to facilitate communication between them is a long-sought goal. Although many gesture solutions have been presented, none of them cope entirely with wrong gesture recognition. This study proposes a novel electromyography (EMG) prototype sensor to capture gestures and also algorithms and procedures to discriminate data containing valid gestures (segmentation). Gestures are recognized using convolutional neural network (CNN) model. The proposed solution presented high recognition accuracy overcoming other similar studies in literature. Test results demonstrated that the proposed solution presents high performance and suggested its use in industrial environment.