Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Tamás Kapelner
  • Ivan Vujaklija

  • Ning Jiang
  • Francesco Negro
  • Oskar C. Aszmann
  • Jose Principe
  • Dario Farina

Research units

  • University of Göttingen
  • University of Waterloo
  • University of Brescia
  • Medical University of Vienna
  • University of Florida
  • Imperial College London

Abstract

Background: Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG. Methods: We recorded wrist kinematics and high-density surface EMG signals from six able-bodied individuals and one patient with limb deficiency while they performed movements of three degrees of freedom of the wrist at three different speeds. We compared the performance of linear regression to predict the observed individual wrist joint angles from, either traditional time domain features of the interference EMG or from motor unit discharge timings (which we termed neural features) obtained by EMG decomposition. In addition, we propose and test a simple model-based dimensionality reduction, based on the physiological notion that the discharge timings of motor units are partly correlated. Results: The regression approach using neural features outperformed regression on classic global EMG features (average R 2 for neural features 0.77 and 0.64, for able-bodied subjects and patients, respectively; for time-domain features 0.70 and 0.52). Conclusions: These results indicate that the use of neural information extracted from EMG decomposition can advance man-machine interfacing for prosthesis control.

Details

Original languageEnglish
Article number47
Number of pages11
JournalJournal of NeuroEngineering and Rehabilitation
Volume16
Issue number1
Publication statusPublished - 5 Apr 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • Prosthesis control, EMG decomposition, Neural information, Motor units, Common drive, Neural drive, EMG signals, Muscle, Decomposition, Interface, Movements, Slow, Time

ID: 33274222