Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks

Abstract--Surface Electromyography (sEMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We propose a model and a deep-learning-based domain adaptation method to approximate the domain shift for recognition accuracy enhancement. Experiments on sparse and High-Density (HD) sEMG datasets validate that our approach outperforms state-of-the-art methods. Traditionally, the control of a graphical user interface of a computer or the actions of a robot or drone is being done with hand or arm gestures interacting with a physical controller, like a mouse in case of traditional 2D screens. A touch sensor in case of touch screens can also be thought of as a physical controller. The wearable devices can keep the possibility to build a Human-Computer Interface (HCI), which gives an universal, natural and easy to use interaction with machines.