{"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/deep-learning-for-electromyographic-hand","title":"Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning","arxiv_id":"1801.07756","date":"2018-01-10","proceeding":null,"authors":["Ulysse Côté-Allard","Cheikh Latyr Fall","Alexandre Drouin","Alexandre Campeau-Lecours","Clément Gosselin","Kyrre Glette","François Laviolette","Benoit Gosselin"],"abstract":"In recent years, deep learning algorithms have become increasingly more\nprominent for their unparalleled ability to automatically learn discriminant\nfeatures from large amounts of data. However, within the field of\nelectromyography-based gesture recognition, deep learning algorithms are seldom\nemployed as they require an unreasonable amount of effort from a single person,\nto generate tens of thousands of examples.\n  This work's hypothesis is that general, informative features can be learned\nfrom the large amounts of data generated by aggregating the signals of multiple\nusers, thus reducing the recording burden while enhancing gesture recognition.\nConsequently, this paper proposes applying transfer learning on aggregated data\nfrom multiple users, while leveraging the capacity of deep learning algorithms\nto learn discriminant features from large datasets. Two datasets comprised of\n19 and 17 able-bodied participants respectively (the first one is employed for\npre-training) were recorded for this work, using the Myo Armband. A third Myo\nArmband dataset was taken from the NinaPro database and is comprised of 10\nable-bodied participants. Three different deep learning networks employing\nthree different modalities as input (raw EMG, Spectrograms and Continuous\nWavelet Transform (CWT)) are tested on the second and third dataset. The\nproposed transfer learning scheme is shown to systematically and significantly\nenhance the performance for all three networks on the two datasets, achieving\nan offline accuracy of 98.31% for 7 gestures over 17 participants for the\nCWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw\nEMG-based ConvNet. Finally, a use-case study employing eight able-bodied\nparticipants suggests that real-time feedback allows users to adapt their\nmuscle activation strategy which reduces the degradation in accuracy normally\nexperienced over time.","url_abs":"http://arxiv.org/abs/1801.07756v5","url_pdf":"http://arxiv.org/pdf/1801.07756v5.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":"deep-learning-for-electromyographic-hand","repo_url":"https://github.com/Giguelingueling/MyoArmbandDataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"deep-learning-for-electromyographic-hand","repo_url":"https://github.com/UlysseCoteAllard/MyoArmbandDataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"deep-learning-for-electromyographic-hand","repo_url":"https://github.com/rajkundu/myoband","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"deep-learning-for-electromyographic-hand","repo_url":"https://github.com/steve30572/myo_armband","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"emg-gesture-recognition","task_name":"EMG Gesture Recognition"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1801.07756","atlas_url":"https://app.syntology.ai/?focus=1801.07756","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1801.07756"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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