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Related Experiment Video

Updated: May 5, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Approaches for retraining sEMG classifiers for upper-limb prostheses.

Tom Donnelly^1,2, Elena Seminati^1,2,3, Benjamin Metcalfe^1,2,4

¹Centre for Accountable, Responsible, and Transparent AI (ART-AI), Department of Computer Science, University of Bath, Bath, United Kingdom.

Frontiers in Neurorobotics

|October 17, 2025

Summary

This summary is machine-generated.

Machine learning for prosthetic control degrades over time. This study introduces three retraining methods to improve accuracy, with signal-to-noise ratio and nearest neighbour approaches showing the best performance for myoelectric prostheses.

Keywords:

hand gesture recognition inter-session retraining machine learning myoelectric prostheses surface electromyography

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Therapy Interventions for Upper Limb Amputees Undergoing Selective Nerve Transfers

Therapy Interventions for Upper Limb Amputees Undergoing Selective Nerve Transfers

Published on: October 29, 2021

Area of Science:

Biomedical Engineering
Rehabilitation Technology
Machine Learning in Prosthetics

Background:

High abandonment rates (44%) for myoelectric upper limb prostheses negatively impact quality of life and increase injury risk.
Conventional signal processing in prosthetics is less robust than machine learning pattern recognition for movement intention.
Surface electromyogram (sEMG) signal non-stationarity and daily variations degrade machine learning classification accuracy, necessitating retraining or adaptation.

Purpose of the Study:

To evaluate three distinct paradigms for retraining machine learning classifiers used in myoelectric prostheses.
To compare the effectiveness of confidence scores, nearest neighbour window assessment, and a novel signal-to-noise ratio (SNR) approach for mitigating accuracy loss.

Main Methods:

The study assessed three retraining paradigms for machine learning-based myoelectric prosthetic control.
Paradigms evaluated included confidence scores, nearest neighbour window assessment, and a novel SNR-based approach.
Effectiveness was measured by intersession accuracy across 10 sessions over 5 days using the NinaPro 6 dataset.

Main Results:

All evaluated retraining paradigms demonstrated improved accuracy compared to no retraining.
The nearest neighbour window assessment and SNR-based approaches yielded a 5% average accuracy improvement over the confidence score method.
These findings highlight the potential of adaptive retraining strategies to enhance prosthetic control reliability.

Conclusions:

Retraining is crucial for maintaining machine learning classifier performance in myoelectric prostheses.
The SNR-based and nearest neighbour methods offer superior accuracy improvements compared to confidence scores.
These advanced retraining techniques can help reduce prosthetic abandonment and improve user outcomes.