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sEMG Gesture Recognition with a Simple Model of Attention

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Document pages: 13 pages

Abstract: Myoelectric control is one of the leading areas of research in the field ofrobotic prosthetics. We present our research in surface electromyography (sEMG)signal classification, where our simple and novel attention-based approach nowleads the industry, universally beating more complex, state-of-the-art models.Our novel attention-based model achieves benchmark leading results on multipleindustry-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 sEMGrepresents a promising avenue for future machine learning research, withapplications not only in prosthetics, but also in other important areas, suchas diagnosis and prognostication of neurodegenerative diseases, computationallymediated surgeries, and advanced robotic control. We reinforce this suggestionwith extensive ablative studies, demonstrating that a neural network can easilyextract higher order spatiotemporal features from noisy sEMG data collected byaffordable, consumer-grade sensors.

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