Designing for practicality: a personalized and adaptive framework for real-time EMG-based hand motor decoding.

Despite remarkable advances in EMG-based hand motor decoding, developing a practical and reliable decoder for robotic prosthetic hands remains unsolved. This study highlights inter-individual, inter-session, and intra-session variabilities of EMG signals as practical challenges and introduces a novel personalized and adaptive motor decoding framework, designed to mitigate their impact and improve hand motor decoding. A dataset was collected from twelve participants (8 male, 4 female), incorporating EMG signals from three forearm muscles during 20 repetitions of 9 distinct hand motions.