Measuring and monitoring skill learning in closed-loop myoelectric hand prostheses using speed-accuracy tradeoffs

Pranav Mamidanna, Shima Gholinezhad, Dario Farina, Jakob Lund Dideriksen, Strahinja Dosen

Research output: Contribution to journalJournal articleResearchpeer-review

1 Downloads (Pure)

Abstract

Objective.Closed-loop myoelectric prostheses, which combine supplementary sensory feedback and electromyography (EMG) based control, hold the potential to narrow the divide between natural and bionic hands. The use of these devices, however, requires dedicated training. Therefore, it is crucial to develop methods that quantify how users acquire skilled control over their prostheses to effectively monitor skill progression and inform the development of interfaces that optimize this process. Approach.Building on theories of skill learning in human motor control, we measured speed-accuracy tradeoff functions (SAFs) to comprehensively characterize learning-induced changes in skill-as opposed to merely tracking changes in task success across training-facilitated by a closed-loop interface that combined proportional control and EMG feedback. Sixteen healthy participants and one individual with a transradial limb loss participated in a three-day experiment where they were instructed to perform the box-and-blocks task using a timed force-matching paradigm at four specified speeds to reach two target force levels, such that the SAF could be determined. Main results.We found that the participants' accuracy increased in a similar way across all speeds we tested. Consequently, the shape of the SAF remained similar across days, at both force levels. Further, we observed that EMG feedback enabled participants to improve their motor execution in terms of reduced trial-by-trial variability, a hallmark of skilled behavior. We then fit a power law model of the SAF, and demonstrated how the model parameters could be used to identify and monitor changes in skill. Significance.We comprehensively characterized how an EMG feedback interface enabled skill acquisition, both at the level of task performance and movement execution. More generally, we believe that the proposed methods are effective for measuring and monitoring user skill progression in closed-loop prosthesis control.

Original languageEnglish
Article number026008
JournalJournal of Neural Engineering
Volume21
Issue number2
ISSN1741-2560
DOIs
Publication statusPublished - 13 Mar 2024

Bibliographical note

Creative Commons Attribution license.

Keywords

  • Speed-accuracy trade-off
  • myoelectric prosthesis control
  • EMG biofeedback
  • motor skill acquisition
  • closed-loop interfaces
  • Humans
  • Electromyography/methods
  • Artificial Limbs
  • Prosthesis Design
  • Hand
  • Learning
  • Task Performance and Analysis
  • Feedback, Sensory

Fingerprint

Dive into the research topics of 'Measuring and monitoring skill learning in closed-loop myoelectric hand prostheses using speed-accuracy tradeoffs'. Together they form a unique fingerprint.

Cite this