Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks
This addresses domain shift issues in sEMG gesture recognition for applications like prosthetics or human-computer interaction, but appears incremental as it builds on existing domain adaptation techniques.
The paper tackled the problem of inter-session and inter-subject variances in sEMG-based gesture recognition by proposing a deep-learning-based domain adaptation method, which outperformed state-of-the-art methods on sparse and HighDensity sEMG datasets.
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.