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Open AccessArticle

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

1
Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
2
Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1613; https://doi.org/10.3390/s20061613
Received: 5 February 2020 / Revised: 8 March 2020 / Accepted: 9 March 2020 / Published: 13 March 2020
(This article belongs to the Special Issue Current Trends and Confounding Factors in Myoelectric Control)
This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting, myoelectric devices still face challenges in robustness to variability of daily living conditions. The intrinsic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where classification performance was 13% and 20% lower than the controlled setting for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of at least 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control. View Full-Text
Keywords: electromyography; EMG; feature extraction; feature selection; myoelectric control; classification; pattern recognition; prosthetics; wearables; amputee electromyography; EMG; feature extraction; feature selection; myoelectric control; classification; pattern recognition; prosthetics; wearables; amputee
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MDPI and ACS Style

Campbell, E.; Phinyomark, A.; Scheme, E. Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity. Sensors 2020, 20, 1613.

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