Low Power Neuromorphic EMG Gesture Classification. (arXiv:2206.02061v1 [eess.SP] CROSS LISTED)

EMG (Electromyograph) signal based gesture recognition can prove vital for
applications such as smart wearables and bio-medical neuro-prosthetic control.
Spiking Neural Networks (SNNs) are promising for low-power, real-time EMG
gesture recognition, owing to their inherent spike/event driven spatio-temporal
dynamics. In literature, there are limited demonstrations of neuromorphic
hardware implementation (at full chip/board/system scale) for EMG gesture
classification. Moreover, most literature attempts exploit primitive SNNs based
on LIF (Leaky Integrate and Fire) neurons. In this work, we address the
aforementioned gaps …

arxiv classification low power neuromorphic power