{"ID":2888344,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.23474","arxiv_id":"2507.23474","title":"Finger Force Decoding from Motor Units Activity on Neuromorphic Hardware","abstract":"Accurate finger force estimation is critical for next-generation human-machine interfaces. Traditional electromyography (EMG)-based decoding methods using deep learning require large datasets and high computational resources, limiting their use in real-time, embedded systems. Here, we propose a novel approach that performs finger force regression using spike trains from individual motor neurons, extracted from high-density EMG. These biologically grounded signals drive a spiking neural network implemented on a mixed-signal neuromorphic processor. Unlike prior work that encodes EMG into events, our method exploits spike timing on motor units to perform low-power, real-time inference. This is the first demonstration of motor neuron-based continuous regression computed directly on neuromorphic hardware. Our results confirm accurate finger-specific force prediction with minimal energy use, opening new possibilities for embedded decoding in prosthetics and wearable neurotechnology.","short_abstract":"Accurate finger force estimation is critical for next-generation human-machine interfaces. Traditional electromyography (EMG)-based decoding methods using deep learning require large datasets and high computational resources, limiting their use in real-time, embedded systems. Here, we propose a novel approach that perf...","url_abs":"https://arxiv.org/abs/2507.23474","url_pdf":"https://arxiv.org/pdf/2507.23474v1","authors":"[\"Farah Baracat\",\"Giacomo Indiveri\",\"Elisa Donati\"]","published":"2025-07-31T11:55:02Z","proceeding":"cs.NE","tasks":"[\"cs.NE\"]","methods":"[]","has_code":false}