{"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/an-emg-gesture-recognition-system-with","title":"An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier","arxiv_id":"1802.10237","date":"2018-02-28","proceeding":null,"authors":["Ali Moin","Andy Zhou","Abbas Rahimi","Simone Benatti","Alisha Menon","Senam Tamakloe","Jonathan Ting","Natasha Yamamoto","Yasser Khan","Fred Burghardt","Luca Benini","Ana C. Arias","Jan M. Rabaey"],"abstract":"EMG-based gesture recognition shows promise for human-machine interaction.\nSystems are often afflicted by signal and electrode variability which degrades\nperformance over time. We present an end-to-end system combating this\nvariability using a large-area, high-density sensor array and a robust\nclassification algorithm. EMG electrodes are fabricated on a flexible substrate\nand interfaced to a custom wireless device for 64-channel signal acquisition\nand streaming. We use brain-inspired high-dimensional (HD) computing for\nprocessing EMG features in one-shot learning. The HD algorithm is tolerant to\nnoise and electrode misplacement and can quickly learn from few gestures\nwithout gradient descent or back-propagation. We achieve an average\nclassification accuracy of 96.64% for five gestures, with only 7% degradation\nwhen training and testing across different days. Our system maintains this\naccuracy when trained with only three trials of gestures; it also demonstrates\ncomparable accuracy with the state-of-the-art when trained with one trial.","url_abs":"http://arxiv.org/abs/1802.10237v2","url_pdf":"http://arxiv.org/pdf/1802.10237v2.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":"an-emg-gesture-recognition-system-with","repo_url":"https://github.com/a-moin/flexemg","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"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":"one-shot-learning","task_name":"One-Shot Learning"},{"task_slug":"robust-classification","task_name":"Robust classification"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}