CAM-MR-MS based gesture recognition method using sEMG

Abstract

With the continuous concern for the disabled and the elderly, intelligent prosthetics and service robots have been widely applied. This paper provides a method for gesture recognition using forearm surface electromyography (sEMG), including an adaptive channel selection method to simplify the sEMG measurement. Based on the forearm muscle groups corresponding to different movements, surface skin areas are divided, and the Myo bracelet is used to collect sEMG signals from these areas. A method combined with channel attention module, multi-channel relationship feature extraction module and multi-scale skip connection module is built to adaptively select the signals from certain skin areas and recognize the seven gestures during experiment. The comparative experimental results indicate that this method can adaptively extract the optimal channel combination and show effective recognition results. It improved the practicability for the sEMG-based gesture recognition.