{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/domain-adaptation-for-semg-based-gesture","title":"Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks","arxiv_id":"1901.06958","date":"2019-01-21","proceeding":null,"authors":["István Ketykó","Ferenc Kovács","Krisztián Zsolt Varga"],"abstract":"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.","url_abs":"https://arxiv.org/abs/1901.06958v2","url_pdf":"https://arxiv.org/pdf/1901.06958v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"domain-adaptation-for-semg-based-gesture","repo_url":"https://github.com/ketyi/2SRNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"emg-gesture-recognition","task_name":"EMG Gesture Recognition"},{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/gesture-recognition-on-capgmyo-db-a","task":"Gesture Recognition","dataset":"CapgMyo DB-a","model":"2SRNN","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"97.1"},"uses_additional_data":false},{"leaderboard":"/sota/gesture-recognition-on-capgmyo-db-b","task":"Gesture Recognition","dataset":"CapgMyo DB-b","model":"2SRNN","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"97.1"},"uses_additional_data":false},{"leaderboard":"/sota/gesture-recognition-on-capgmyo-db-c","task":"Gesture Recognition","dataset":"CapgMyo DB-c","model":"2SRNN","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"96.8"},"uses_additional_data":false},{"leaderboard":"/sota/gesture-recognition-on-ninapro-db-1-12","task":"Gesture Recognition","dataset":"Ninapro DB-1 12 gestures","model":"2SRNN","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"84.7"},"uses_additional_data":false},{"leaderboard":"/sota/gesture-recognition-on-ninapro-db-1-8","task":"Gesture Recognition","dataset":"Ninapro DB-1 8 gestures","model":"2SRNN","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"90.7"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}