🤖 AI Summary
To address the limited degrees of freedom (DoFs) and poor robustness in myoelectric prosthetic control for upper-limb amputees, this paper proposes a context-aware biosignal decoding framework. Specifically, it explicitly models task context—including grasp targets and movement intent—and integrates it into surface electromyography (sEMG) pattern recognition via temporal modeling (LSTM/Transformer), multimodal context encoding, and adaptive transfer learning. The method significantly improves motion classification accuracy and cross-subject/cross-session generalizability. In real-user evaluations, it achieves a mean recognition accuracy of 96.2%, an end-to-end latency of <120 ms, and enables real-time online switching among eight dexterous hand gestures. This work establishes a novel paradigm for natural, robust, and high-DoF prosthetic control.