Muscle Synergy-driven Motor Unit Clustering for Human-Machine Interfacing.

Electromyographic signals (EMGs) can provide information on the overall activity of the innervating motor neuros in any given muscle but also globally reflect the underlying neuromechanics of human movement (e.g., muscle synergies). motor unit(MU) decomposition is a technique based on the deconvolution of high-density EMGs (HD-EMG) in order to derive the activities of the corresponding motor neurons. This powerful yet very sensitive tool has seen some traction in human-machine interfacing (HMI) for rehabilitation. Here, we propose combining the synergy-inspired channel clustering in order to isolate the most prominent regions of EMG activation in each targeted degree of freedom (DoF) and thus cater to decomposition's sensitivity demands. Our assumption is that this will lead to a higher number of extracted MUs and consequently better motion estimation in HMIs. Indeed, in four subjects, we have shown a 69% average increase in the number of MUs when decomposition was done using muscle-synergy channel clustering. Consequently, all three of our kinematic estimators benefited from an increased pool of units, with the linear regressor showing the greatest improvement once compared to, the artificial neural network and the gated recurrent unit, which had the overall best performance. Clinical Relevance- The results demonstrated in this work provide a new perspective on the online EMG-driven HMI systems that can be greatly beneficial in the rehabilitation of motor disorders.