Towards Unsupervised Incremental and Proportional Myocontrol Based on Higher-Density Surface Electromyography

Upper limb differences present tremendous challenges for autonomy in daily living, and current prostheses often face high abandonment rates due to complexity and lack of functionality. This study investigates fully unsupervised incremental myocontrol using higher-density surface EMG. Utilizing incremental sparse non-negative matrix factorization (ISNMF), we employed two 32-channel sEMG bracelets to incrementally extract muscle synergies from EMG signals in real-time. Eight able-bodied participants underwent this unsupervised training paradigm with an increasing number of target synergies and were evaluated with a virtual target achievement control (TAC) test. Participants demonstrated up to six independently controllable synergies in full-intensity tasks, exceeding the current state of the art. However, proportional control remained challenging, reflected in a median success rate of 10% for half-intensity targets. Subjective feedback across the number of synergies showed only small variations in cognitive and physical workload despite increased complexity. This approach shows promise for enabling fully unsupervised myocontrol, but further refinement of training protocol and hyperparameters, as well as testing on users with limb differences, are necessary to validate and improve this approach.