A Wearable Platform for Real-Time Control of a Prosthetic Hand by High-Density EMG

This study presents a heterogeneous embedded architecture that addresses a fundamental gap in wearable myoelectric systems: the inability of existing platforms to simultaneously provide high-density signal acquisition, computational flexibility, and autonomy. The platform integrates two 64-channel RHD2164 front-ends (128 channels total) with a Zynq UltraScale+ multiprocessor system-on-chip for heterogeneous processing. A PYNQ-based Python/Linux framework enables scalable algorithm development. Experiments with 21 healthy subjects performing eight motor tasks (finger flexion/extension, thumb opposition, and grasp patterns) at two frequencies (0.50 and 0.75 Hz) demonstrated the platforms capability in high-density surface electromyography (HD sEMG) recording and real-time control of a single degree of freedom (1-DoF). Signal quality exceeded recommended thresholds (Signal-to-Noise Ratio: 13.93 {+/-} 7.51 dB; Signal-to-Motion-artifact Ratio: 25.18 {+/-} 5.18 dB), confirming the effectiveness of the dual-front-end architecture. The processing pipeline combined reinforced electrode signal adaptation (RESA), non-negative matrix factorization (NMF), and Kalman filtering, resulting in strong agreement between estimated and reference signals, with maximum normalized cross-correlation (XCmax) values from 0.54 {+/-} 0.22 to 0.80 {+/-} 0.16. The coefficient of determination (R2) for HD sEMG reconstruction ranged from 0.87 {+/-} 0.09 to 0.95 {+/-} 0.03, with higher values for prehension tasks. End-to-end latency from acquisition to command output ranged from 63.3 {+/-} 1.0 ms (30 ms buffer) to 219.1 {+/-} 4.5 ms (150 ms buffer), maintaining temporal alignment (XCmax lag: 0.02 {+/-} 2.09 s). The heterogeneous architecture supports full local processing, with the FPGA handling acquisition and the Arm Cortex-A53 cores performing motor intention decoding, providing a scalable foundation for adaptive multi-DoF prosthetic control.