Looking beyond proportional control: The relevance of mode switching in learning to operate multi-articulating myoelectric upper-limb prostheses

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Abstract

Objective: Operating a multi-articulating myoelectric prosthetic hand requires both proportional control of surface electromyography (sEMG) signals and accurately producing triggers to switch between grip types. Proportional control, used for hand opening/closing, has been studied extensively but the grip-switching control far less so. Can switching be trained and if so, how early on can improvement be seen? And is the ability to produce precise switches related to proficiency in proportional control? We examined individual learning differences, monitored improvements in switching control, and assessed whether skilful proportional control predicts proficient switching. Methods: Twenty-three unimpaired participants fitted with sEMG electrodes on the wrist flexors/extensors performed two 10-minute tasks: an adaptive virtual catching task (proportional control) and an sEMG- feedback-based switching task, with multiple performance measures being recorded and compared. Results: Most outcome measures revealed large individual differences, with 47–87% of the participants showing improvement in switching and 14% deteriorating. As hypothesized, no correlations were found between mode-switching and proportional-control skills. Conclusion: Learning to generate accurate grip-switching control proved difficult and might not be feasible for all individuals with upper-limb amputations. Unaffected participants showing proficient proportional control were not automatically good at producing well-tuned mode switches. Significance: In rehabilitation, clients with good proportional control are often offered a multi-articulating hand prosthesis. Since we found no clear associations between the proficiency in proportional movements and mode switches, this rationale may warrant reconsideration. Rather, when selecting the most optimal prosthesis, an individual's skills in both control types should be examined separately.

Original languageEnglish
Article number101647
Number of pages9
JournalBiomedical signal processing and control
Volume55
DOIs
Publication statusPublished - Jan-2020

Keywords

  • Electromyography
  • Motor learning
  • Myoelectric control
  • Perception-action
  • Prosthesis

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