sEMGCareHCI: sEMG-based finger position prediction for amputee-centric human-computer interaction
sEMGCareHCI presents a low-cost, non-invasive surface electromyography (sEMG)-based system for recognizing finger positions and gestures from forearm muscle activity. The proposed “Spatio-Temporal Attention Model (STAM)” combines handcrafted time-domain features, autoencoder-derived latent representations, and continuo...