User training for machine learning controlled upper limb prostheses: a serious game approach.

Journal: Journal of neuroengineering and rehabilitation
PMID: 33579326

Abstract

BACKGROUND: Upper limb prosthetics with multiple degrees of freedom (DoFs) are still mostly operated through the clinical standard Direct Control scheme. Machine learning control, on the other hand, allows controlling multiple DoFs although it requires separable and consistent electromyogram (EMG) patterns. Whereas user training can improve EMG pattern quality, conventional training methods might limit user potential. Training with serious games might lead to higher quality EMG patterns and better functional outcomes. In this explorative study we compare outcomes of serious game training with conventional training, and machine learning control with the users' own one DoF prosthesis.

Authors

  • Morten B Kristoffersen
    Department of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden; Integrum AB, Mölndal, Sweden.
  • Andreas W Franzke
  • Raoul M Bongers
    Center for Human Movement Sciences, University of Groningen, University Medical Center Groningen Groningen, Netherlands.
  • Michael Wand
    Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
  • Alessio Murgia
  • Corry K van der Sluis
    Department of Rehabilitation Medicine, University of Groningen, University Medical Center Groningen Groningen, Netherlands.

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