Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks
TL
TL;DR
Analysis performed on sparse and High-Density sEMG public datasets validate that the proposed model and deep-learning-based domain adaptation method outperforms state-of-the-art methods on recognition accuracy enhancement.
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. Analysis performed on sparse and High-Density (HD) sEMG public datasets validate that our approach outperforms state-of-the-art methods.
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