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

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

21 Jan 2019arXiv:1901.06958archive 2025-07-28

István Ketykó, Ferenc Kovács, Krisztián Zsolt Varga

Surface Electromyography (sEMG/EMG) 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 HighDensity (HD) sEMG public datasets validate that our approach outperforms state-of-the-art methods.

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