{"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/semg-gesture-recognition-with-a-simple-model","title":"sEMG Gesture Recognition with a Simple Model of Attention","arxiv_id":"2006.03645","date":"2020-06-05","proceeding":null,"authors":["David Josephs","Carson Drake","Andrew Heroy","John Santerre"],"abstract":"Myoelectric control is one of the leading areas of research in the field of robotic prosthetics. We present our research in surface electromyography (sEMG) signal classification, where our simple and novel attention-based approach now leads the industry, universally beating more complex, state-of-the-art models. Our novel attention-based model achieves benchmark leading results on multiple industry-standard datasets including 53 finger, wrist, and grasping motions, improving over both sophisticated signal processing and CNN-based approaches. Our strong results with a straightforward model also indicate that sEMG represents a promising avenue for future machine learning research, with applications not only in prosthetics, but also in other important areas, such as diagnosis and prognostication of neurodegenerative diseases, computationally mediated surgeries, and advanced robotic control. We reinforce this suggestion with extensive ablative studies, demonstrating that a neural network can easily extract higher order spatiotemporal features from noisy sEMG data collected by affordable, consumer-grade sensors.","url_abs":"https://arxiv.org/abs/2006.03645v2","url_pdf":"https://arxiv.org/pdf/2006.03645v2.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":"semg-gesture-recognition-with-a-simple-model","repo_url":"https://github.com/josephsdavid/semg_repro","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"semg-gesture-recognition-with-a-simple-model","repo_url":"https://github.com/josephsdavid/attentionsemg","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"emg-gesture-recognition","task_name":"EMG Gesture Recognition"},{"task_slug":"electromyography-emg","task_name":"Electromyography (EMG)"},{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fcn","method_name":"FCN"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"lookahead","method_name":"Lookahead"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"radam","method_name":"RAdam"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2006.03645","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.03645"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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