MARC: Asymmetric Memory Coupling for Myoelectric Hand Motion Decoding

Streaming hand motion decoding from surface electromyography (EMG) benefits from motion history, but feeding predicted poses back into memory can also propagate their errors. We study how an explicit motion predictor should interact with persistent sensor memory. MARC combines joint-conditioned EMG encoding, observation-driven memory updates, and pose-conditioned motion prediction. Predictions contribute to the decoded pose without writing back into observation memory, giving the two state pathways an asymmetric dependency. Across emg2pose, MANUS, and NinaPro DB8, MARC improves mean reconstruction accuracy over reproduced TDS+LSTM and EMGFormer-L baselines. On emg2pose, it reduces angular error by 9.5–19.3% and fingertip error by 13.1–21.6% relative to TDS+LSTM, while improving displacement accuracy and motion direction. Matched feedback controls and complementary state interventions reveal a trade-off: restricting pose feedback reduces the output response to an injected pose error, but slows recovery of the unperturbed output after observation-memory erasure. Together, these results establish the practical value of MARC and identify prediction-to-memory feedback as a testable design choice in continuous myoelectric decoding.