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Journal of NeuroEngineering and Rehabilitation
Published in: Journal of NeuroEngineering and Rehabilitation 1/2010

Open Access 01-12-2010 | Research

Study of stability of time-domain features for electromyographic pattern recognition

Authors: Dennis Tkach, He Huang, Todd A Kuiken

Published in: Journal of NeuroEngineering and Rehabilitation | Issue 1/2010

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Abstract

Background

Significant progress has been made towards the clinical application of human-machine interfaces (HMIs) based on electromyographic (EMG) pattern recognition for various rehabilitation purposes. Making this technology practical and available to patients with motor deficits requires overcoming real-world challenges, such as physical and physiological changes, that result in variations in EMG signals and systems that are unreliable for long-term use. In this study, we aimed to address these challenges by (1) investigating the stability of time-domain EMG features during changes in the EMG signals and (2) identifying the feature sets that would provide the most robust EMG pattern recognition.

Methods

Variations in EMG signals were introduced during physical experiments. We identified three disturbances that commonly affect EMG signals: EMG electrode location shift, variation in muscle contraction effort, and muscle fatigue. The impact of these disturbances on individual features and combined feature sets was quantified by changes in classification performance. The robustness of feature sets was evaluated by a stability index developed in this study.

Results

Muscle fatigue had the smallest effect on the studied EMG features, while electrode location shift and varying effort level significantly reduced the classification accuracy for most of the features. Under these disturbances, the most stable EMG feature set with combination of four features produced at least 16.0% higher classification accuracy than the least stable set. EMG autoregression coefficients and cepstrum coefficients showed the most robust classification performance of all studied time-domain features.

Conclusions

Selecting appropriate EMG feature combinations can overcome the impact of the studied disturbances on EMG pattern classification to a certain extent; however, this simple solution is still inadequate. Stabilizing electrode contact locations and developing effective classifier training strategies are suggested to further improve the robustness of HMIs based on EMG pattern recognition.
Appendix
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Metadata
Title
Study of stability of time-domain features for electromyographic pattern recognition
Authors
Dennis Tkach
He Huang
Todd A Kuiken
Publication date
01-12-2010
Publisher
BioMed Central
Published in
Journal of NeuroEngineering and Rehabilitation / Issue 1/2010
Electronic ISSN: 1743-0003
DOI
https://doi.org/10.1186/1743-0003-7-21

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