AI RESEARCH
Decoding High-Dimensional Finger Motion from EMG Using Riemannian Features and RNNs
arXiv CS.LG
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ArXi:2604.22499v1 Announce Type: new Continuous estimation of high-dimensional finger kinematics from forearm surface electromyography (EMG) could enable natural control for hand prostheses, AR/XR interfaces, and teleoperation. However, the complexity of human hand gestures and the entanglement of forearm muscles make accurate recognition intrinsically challenging. Existing approaches typically reduce task complexity by relying on classification-based machine learning, limiting the controllable degrees of freedom and compromising on natural interaction.