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

Autor:	Ketykó, István, Kovács, Ferenc, Varga, Krisztián Zsolt
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Computer Science - Human-Computer Interaction Statistics - Machine Learning
Zdroj:	2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 2019, pp. 1-7
Druh dokumentu:	Working Paper
DOI:	10.1109/IJCNN.2019.8852018
Popis:	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. Comment: Typos corrected
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1901.06958 Zobrazit plný text záznamu View this record from Arxiv