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Related Experiment Video

Updated: Jul 14, 2025

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Instance-based learning with prototype reduction for real-time proportional myocontrol: a randomized user study

Tim Sziburis1,2, Markus Nowak3, Davide Brunelli4

  • 1Institute for Neuroinformatics (INI), Ruhr University Bochum, Universitätsstr. 150, Bochum, 44801, Germany. tim.sziburis@alumni.cern.

Medical & Biological Engineering & Computing
|October 5, 2023
PubMed
Summary

k-Nearest Neighbors (kNN) methods show superior performance in prosthetic control gesture detection compared to regression techniques. Dataset reduction using Decision Surface Mapping (DSM) maintains high accuracy with significantly reduced computational load for wearable devices.

Keywords:
Data reductionEMGEmbedded systemsMachine learningProsthetic control

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Rehabilitation Technology

Background:

  • Gesture detection is crucial for advanced prosthetic control.
  • High computational demands of instance-based learning hinder real-time application in portable devices.
  • Dataset reduction techniques are essential for efficient embedded control systems.

Purpose of the Study:

  • To design, implement, and validate kNN-based learning techniques for gesture detection in prosthetic control.
  • To evaluate dataset reduction methods for real-time determinism in battery-powered devices.
  • To compare the performance of kNN with Decision Surface Mapping (DSM) against regression techniques.

Main Methods:

  • Utilized an eight-channel-sEMG armband for data acquisition.
  • Evaluated dataset reduction techniques, focusing on Decision Surface Mapping (DSM) for kNN.
  • Conducted offline cross-validation and real-time pilot experiments (online target achievement tests).
  • Performed a randomized, double-blind user study comparing kNN (with and without DSM) against Ridge Regression (RR) and RR with Random Fourier Features (RR-RFF).

Main Results:

  • kNN-based methods significantly outperformed regression techniques ([Formula: see text]).
  • DSM-kNN achieved over 99% dataset reduction with no statistically significant difference in success rate compared to standard kNN.
  • Runtime complexity for both kNN and DSM-kNN became linear with the original sample size using an optimal parameter ([Formula: see text]).

Conclusions:

  • kNN-based learning techniques, particularly with DSM dataset reduction, are highly effective for real-time prosthetic gesture detection.
  • DSM significantly reduces computational load, enabling reliable integration into wearable prosthetic devices.
  • The developed methods offer a promising, efficient, and accurate solution for advanced prosthetic control systems.