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Related Experiment Video

Updated: Nov 17, 2025

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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User training for machine learning controlled upper limb prostheses: a serious game approach.

Morten B Kristoffersen1, Andreas W Franzke2, Raoul M Bongers3

  • 1Department of Rehabilitation Medicine, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. more-ten@protonmail.com.

Journal of Neuroengineering and Rehabilitation
|February 13, 2021
PubMed
Summary
This summary is machine-generated.

Serious game training for upper limb prosthetics showed similar results to conventional methods, with no consistent improvements in EMG patterns or function. Participants performed better with their own direct-controlled prosthesis than the machine learning-controlled one.

Keywords:
EMGMachine learningMotor learningProsthesisSerious gamesStructured training

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Science
  • Human-Computer Interaction

Background:

  • Current upper limb prosthetics primarily use Direct Control, limiting multi-degree of freedom (DoF) operation.
  • Machine learning (ML) control offers multi-DoF capabilities but requires distinct electromyogram (EMG) patterns.
  • User training is crucial for EMG pattern quality, with serious games potentially enhancing outcomes over conventional methods.

Purpose of the Study:

  • To compare the efficacy of serious game-based training versus conventional training for ML-controlled upper limb prosthetics.
  • To evaluate ML control against users' existing direct-controlled prostheses.

Main Methods:

  • Eight participants with upper limb absence underwent 7 training sessions for a 3-DoF prosthesis.
  • Training was divided into conventional coaching or serious game-based approaches, focusing on EMG pattern separability and functional use.
  • A neural network regressor controlled the prosthesis, with outcomes measured by EMG metrics, DoFs used, and functional tests (Southampton Hand Assessment Procedure, Clothespin Relocation Test).

Main Results:

  • Four participants completed the study; training did not yield consistent improvements in EMG pattern quality or functional use across groups.
  • Some participants showed metric-specific improvements, but no significant differences were found between training groups.
  • Participants consistently performed better with their own 1-DoF direct-controlled prosthesis compared to the study's ML-controlled prosthesis.

Conclusions:

  • Serious game training appears comparable to conventional training for ML-controlled prosthetics in this small, exploratory study.
  • Insufficient training duration may explain the lack of consistent functional or EMG improvements.
  • Further research is needed on user training for ML prosthetics, with this study adding data on ML versus Direct Control comparisons.