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Open AccessArticle

Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System

1
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen 518055, China
2
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
3
School of Computing Science and Engineering, VIT University, Vellore 632014, Tamil Nadu, India
4
SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, CAS, Shenzhen 518055, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Symmetry 2020, 12(10), 1710; https://doi.org/10.3390/sym12101710
Received: 13 September 2020 / Revised: 10 October 2020 / Accepted: 12 October 2020 / Published: 16 October 2020
(This article belongs to the Special Issue Symmetric and Asymmetric Data in Solution Models)
The constantly rising number of limb stroke survivors and amputees has motivated the development of intelligent prosthetic/rehabilitation devices for their arm function restoration. The device often integrates a pattern recognition (PR) algorithm that decodes amputees’ limb movement intent from electromyogram (EMG) signals, characterized by neural information and symmetric distribution. However, the control performance of the prostheses mostly rely on the interrelations among multiple dynamic factors of feature set, windowing parameters, and signal conditioning that have rarely been jointly investigated to date. This study systematically investigated the interaction effects of these dynamic factors on the performance of EMG-PR system towards constructing optimal parameters for accurately robust movement intent decoding in the context of prosthetic control. In this regard, the interaction effects of various features across window lengths (50 ms~300 ms), increments (50 ms~125 ms), robustness to external interferences and sensor channels (2 ch~6 ch), were examined using EMG signals obtained from twelve subjects through a symmetrical movement elicitation protocol. Compared to single features, multiple features consistently achieved minimum decoding error below 10% across optimal windowing parameters of 250 ms/100 ms. Also, the multiple features showed high robustness to additive noise with obvious trade-offs between accuracy and computation time. Consequently, our findings may provide proper insight for appropriate parameter selection in the context of robust PR-based control strategy for intelligent rehabilitation device. View Full-Text
Keywords: rehabilitation device; electromyogram; symmetry; window parameters; feature extraction; pattern recognition rehabilitation device; electromyogram; symmetry; window parameters; feature extraction; pattern recognition
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MDPI and ACS Style

Asogbon, M.G.; Samuel, O.W.; Jiang, Y.; Wang, L.; Geng, Y.; Sangaiah, A.K.; Chen, S.; Fang, P.; Li, G. Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System. Symmetry 2020, 12, 1710.

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