Offline and online myoelectric pattern recognition analysis and real-time control of a robotic hand after spinal cord injury (original) (raw)

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Published 16 April 2019 • © 2019 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Journal of Neural Engineering,Volume 16,Number 3Citation Zhiyuan Lu et al 2019 J. Neural Eng. 16 036018DOI 10.1088/1741-2552/ab0cf0

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1741-2552/16/3/036018

Abstract

Objective. The objective of this study was to investigate the feasibility of applying myoelectric pattern recognition for controlling a robotic hand in individuals with spinal cord injury (SCI). Approach. Surface electromyogram (sEMG) signals of six hand motion patterns were recorded from 12 subjects with SCI. Online and offline classification performance of two classifiers (Gaussian Naive Bayes classifier, GNB, and support vector machine, SVM) were investigated. An exoskeleton hand was then controlled in real-time using the classification results. The control accuracy and its correlation with function assessments were investigated. Main results. Average offline classification accuracy of all tested SCI subjects was (73.6 ± 14.0)% for GNB and (77.6 ± 11.6)% for SVM, respectively. Average online classification accuracy was significantly lower, (64.3 ± 15.0)% for GNB and (70.2 ± 13.2)% for SVM. Average control accuracy of (81.0 ± 16.3)% was achieved in real-time control of the robotic hand using myoelectric pattern recognition. Correlation between control accuracy and grip/pinch force was observed. Significance. The results show that it is feasible to extract hand motion intent from individuals with SCI and control a robotic hand device using myoelectric pattern recognition. The performance of real-time control can be predicted based on functional assessments.

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