Learning regularized representations of categorically labelled surface EMG enables simultaneous and proportional myoelectric control

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Abstract

Background: Processing the surface electromyogram (sEMG) to decode movement intent is a promising approach for natural control of upper extremity prostheses. To this end, this paper introduces and evaluates a new framework which allows for simultaneous and proportional myoelectric control over multiple degrees of freedom (DoFs) in real-time. The framework uses multitask neural networks and domain-informed regularization in order to automatically find nonlinear mappings from the forearm sEMG envelope to multivariate and continuous encodings of concurrent hand- and wrist kinematics, despite only requiring categorical movement instruction stimuli signals for calibration. Methods: Forearm sEMG with 8 channels was collected from healthy human subjects (N = 20) and used to calibrate two myoelectric control interfaces, each with two output DoFs. The interfaces were built from (I) the proposed framework, termed Myoelectric Representation Learning (MRL), and, to allow for comparisons, from (II) a standard pattern recognition framework based on Linear Discriminant Analysis (LDA). The online performances of both interfaces were assessed with a Fitts’s law type test generating 5 quantitative performance metrics. The temporal stabilities of the interfaces were evaluated by conducting identical tests without recalibration 7 days after the initial experiment session. Results: Metric-wise two-way repeated measures ANOVA with factors method (MRL vs LDA) and session (day 1 vs day 7) revealed a significant (p< 0.05) advantage for MRL over LDA in 5 out of 5 performance metrics, with metric-wise effect sizes (Cohen’s d) separating MRL from LDA ranging from | d| = 0.62 to | d| = 1.13. No significant effect on any metric was detected for neither session nor interaction between method and session, indicating that none of the methods deteriorated significantly in control efficacy during one week of intermission. Conclusions: The results suggest that MRL is able to successfully generate stable mappings from EMG to kinematics, thereby enabling myoelectric control with real-time performance superior to that of the current commercial standard for pattern recognition (as represented by LDA). It is thus postulated that the presented MRL approach can be of practical utility for muscle-computer interfaces.

Original languageEnglish
Article number35
Number of pages19
JournalJournal of NeuroEngineering and Rehabilitation
Volume18
DOIs
Publication statusPublished - 2021 Feb 15

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).

Subject classification (UKÄ)

  • Biomedical Laboratory Science/Technology

Keywords

  • Deep learning
  • Electromyography
  • Multitask learning
  • Online performance
  • Prosthetic control
  • Regression
  • Regularization
  • Representation learning

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