Using surface electromyography to predict single finger forces

Surface electromyography (sEMG) of the forearm is an active research topic since the 1990s in the rehabilitation robotics/machine learning community, as it can be used to predict the hand posture and overall grip force. We hereby advance the state of the art by describing a multi-subject experiment in which sEMG is successfully used to predict simultaneous forces applied by a human subject at the fingertips, that is, when six voluntary muscle contractions (VMCs) are elicited (flexion of the little, ring, middle and index fingers, thumb rotation and thumb adduction). Using a multi-sensor setup sEMG activity of the forearm of a human subject and the forces exerted at the fingertips are measured; a Support Vector Machine is then used to associate sEMG signals and forces. Our results clearly show that sEMG can be used to predict the required forces with an error as small as 1.5% of the sensor range. Targeted positioning of the electrodes is not required. The prediction is uniformly accurate across all VMCs and all 12 subjects considered, and it is robust against subsampling. This result goes in the direction of enabling natural force/impedance control of a highly dexterous prosthetic hand over a continuous, infinite manifold of force configurations, rather than using posture classification like in the traditional approach.