Individuals With Upper Limb Loss Require Minimal Training to Achieve Robust Motion Classification Using Sonomyography

Abstract

Background: Although surface electromyography is commonly used as a sensing strategy for upper limb prostheses, it remains difficult to reliably decode the recorded signals for controlling multi-articulated hands. Sonomyography, or ultrasound-based sensing of muscle deformation, overcomes some of these issues and allows individuals with upper limb loss to reliably perform multiple motion patterns. The purposes of this study were to determine 1) the effect of training on classification performance with sonomyographic control and 2) the effect of training on the underlying muscle deformation patterns.Methods: A series of motion pattern datasets were collected from five individuals with transradial limb loss. Each dataset contained five ultrasound images corresponding each of the following five motions: power grasp, wrist pronation, key grasp, tripod, point. Participants initially performed the motions for the datasets without receiving feedback on their performance (baseline phase), then with visual and verbal feedback (feedback phase), and finally again without feedback (retention phase). Cross-validation accuracy and metrics describing the consistency and separability of the muscle deformation patters were computed for each dataset. Changes in classification performance over the course of the study were assessed using linear mixed models. Associations between classification performance and the consistency and separability metrics were evaluated using Pearson correlations.Results: The average cross-validation accuracy for each phase was 92% or greater and there was no significant change in cross-validation accuracy throughout training. Misclassifications of one motion as another did not persist systematically across datasets. Few of the correlations were significant, although many were moderate or greater in strength and showed a positive association between accuracy and improved consistency and separability metrics.Conclusions: Participants were able to achieve high classification rates upon their initial exposure to sonomyography and training did not affect their performance. Thus, motion classification using sonomyography may be highly intuitive and is unlikely to require a structured training protocol to gain proficiency.

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

“…In particular, our group has demonstrated the ability to classify five individual digit movements with 97% accuracy [27] and 15 complex grasps with 91% accuracy [28] in able-bodied individuals. There has been relatively little exploration of the use of SMG in individuals with upper limb loss [29–33], although we have reported an average classification accuracy of 96% for five grasps in this population [32] with minimal training required [33].…”