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Sensors 2018, 18(5), 1615; https://doi.org/10.3390/s18051615

Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors

1
Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
2
Faculty of Engineering and Information Technology, University of Technology, Sydney 2007, NSW, Australia
*
Author to whom correspondence should be addressed.
Received: 14 March 2018 / Revised: 27 April 2018 / Accepted: 16 May 2018 / Published: 18 May 2018
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Abstract

Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent advancements in wearable sensors, wireless communication and embedded technologies, wearable electromyographic (EMG) armbands are now commercially available for the general public. Due to physical, processing, and cost constraints, however, these armbands typically sample EMG signals at a lower frequency (e.g., 200 Hz for the Myo armband) than their clinical counterparts. It remains unclear whether existing EMG feature extraction methods, which largely evolved based on EMG signals sampled at 1000 Hz or above, are still effective for use with these emerging lower-bandwidth systems. In this study, the effects of sampling rate (low: 200 Hz vs. high: 1000 Hz) on the classification of hand and finger movements were evaluated for twenty-six different individual features and eight sets of multiple features using a variety of datasets comprised of both able-bodied and amputee subjects. The results show that, on average, classification accuracies drop significantly ( p < 0.05) from 2% to 56% depending on the evaluated features when using the lower sampling rate, and especially for transradial amputee subjects. Importantly, for these subjects, no number of existing features can be combined to compensate for this loss in higher-frequency content. From these results, we identify two new sets of recommended EMG features (along with a novel feature, L-scale) that provide better performance for these emerging low-sampling rate systems. View Full-Text
Keywords: electromyography; EMG; feature extraction; L-moments; pattern recognition; prosthesis; sampling rate; wearable sensor electromyography; EMG; feature extraction; L-moments; pattern recognition; prosthesis; sampling rate; wearable sensor
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Phinyomark, A.; N. Khushaba, R.; Scheme, E. Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors. Sensors 2018, 18, 1615.

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