Assessing Neuromorphic Computing for Fingertip Force Decoding from Electromyography

• URL: http://arxiv.org/abs/2512.10179v1
• Date: Thu, 11 Dec 2025 00:33:31 GMT
• Title: Assessing Neuromorphic Computing for Fingertip Force Decoding from Electromyography
• Authors: Abolfazl Shahrooei, Luke Arthur, Om Patel, Derek Kamper,
• Abstract summary: High-density surface electromyography (HD-sEMG) provides a noninvasive neural interface for assistive and rehabilitation control.<n>We assess a spiking neural network (SNN) as a neuromorphic architecture against a temporal convolutional network (TCN) for decoding fingertip force from motor-unit firing.
• Score: 0.09999629695552194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-density surface electromyography (HD-sEMG) provides a noninvasive neural interface for assistive and rehabilitation control, but mapping neural activity to user motor intent remains challenging. We assess a spiking neural network (SNN) as a neuromorphic architecture against a temporal convolutional network (TCN) for decoding fingertip force from motor-unit (MU) firing derived from HD-sEMG. Data were collected from a single participant (10 trials) with two forearm electrode arrays; MU activity was obtained via FastICA-based decomposition, and models were trained on overlapping windows with end-to-end causal convolutions. On held-out trials, the TCN achieved 4.44% MVC RMSE (Pearson r = 0.974) while the SNN achieved 8.25% MVC (r = 0.922). While the TCN was more accurate, we view the SNN as a realistic neuromorphic baseline that could close much of this gap with modest architectural and hyperparameter refinements.

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