Finger Force Decoding from Motor Units Activity on Neuromorphic Hardware

• URL: http://arxiv.org/abs/2507.23474v1
• Date: Thu, 31 Jul 2025 11:55:02 GMT
• Title: Finger Force Decoding from Motor Units Activity on Neuromorphic Hardware
• Authors: Farah Baracat, Giacomo Indiveri, Elisa Donati,
• Abstract summary: We propose a novel approach to perform finger force regression using spike trains from individual motor neurons.<n>These biologically grounded signals drive a spiking neural network implemented on a mixed-signal neuromorphic processor.<n>This is the first demonstration of motor neuron-based continuous regression computed directly on neuromorphic hardware.
• Score: 1.8754256211583082
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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