Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition. (arXiv:2203.12932v2 [eess.SP] UPDATED)

Human-machine interaction is gaining traction in rehabilitation tasks, such
as controlling prosthetic hands or robotic arms. Gesture recognition exploiting
surface electromyographic (sEMG) signals is one of the most promising
approaches, given that sEMG signal acquisition is non-invasive and is directly
related to muscle contraction. However, the analysis of these signals still
presents many challenges since similar gestures result in similar muscle
contractions. Thus the resulting signal shapes are almost identical, leading to
low classification accuracy. To tackle this challenge, complex …

arxiv embedding gesture recognition low power power transformers