Motion prediction using electromyography and sonomyography for an individual with transhumeral limb loss

Abstract Controlling multi-articulated prosthetic hands with surface electromyography can be challenging for users. Sonomyography, or ultrasound-based sensing of muscle deformation, avoids some of the problems of electromyography and enables classification of multiple motion patterns in individuals with upper limb loss. Because sonomyography has been previously studied only in individuals with transradial limb loss, the purpose of this study was to assess the feasibility of an individual with transhumeral limb loss using this modality for motion classification. A secondary aim was to compare motion classification performance between electromyography and sonomyography. A single individual with transhumeral limb loss created two datasets containing 11 motions each (individual flexion of each finger, thumb abduction, power grasp, key grasp, tripod, point, pinch, wrist pronation). Electromyography or sonomyography signals associated with every motion were acquired and cross-validation accuracy was computed for each dataset. While all motions were usually predicted successfully with both electromyography and sonomyography, the cross-validation accuracies were typically higher for sonomyography. Although this was an exploratory study, the results suggest that controlling an upper limb prosthesis using sonomyography may be feasible for individuals with transhumeral limb loss.