Wrist EMG-based Gestures Recognition for Finger and Wrist Motions.

Gesture recognition is a relatively natural humanmachine interface (HMI). Electromyography (EMG) based gesture recognition methods have been extensively investigated in upper limb prostheses as a special HMI, in which EMG sensors are mostly mounted on the proximal part of the forearm. However, for more general applications beyond upper-limb prosthetics, the wrist may be a more suitable position for more intuitive HMIs, in which independent finger movements in addition to wrist gestures can be realized. In this study, we propose to investigate the recognition performance for gestures of the index finger using wrist EMG. All DOFs of the metacarpophalangeal joint, including static and dynamic gestures with directions, were investigated. Forearm EMG and conventional wrist motions were used as controls for wrist EMG and finger motions, respectively. The frequency division technique (FDT) was first adopted for feature extraction of wrist EMG signals. Finally, three combinations of algorithm and feature were applied to gesture recognition. Results showed that linear discriminate analysis (LDA) and FDT using wrist EMG had a mean classification accuracy of 79% and 89% for static finger and wrist gestures, respectively, and for forearm EMG, the corresponding values were 73% and 90%. A potential biomedical application is to assist patients with index finger disability with the unobtrusive wrist-worn band.