Zhiyuan Lu1, Argyrios Stampas, Gerard E Francisco, Ping Zhou. 1. Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center, Houston, TX, United States of America. TIRR Memorial Hermann Research Center, Houston, TX, United States of America.
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.
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.
Authors: Jordyn E Ting; Alessandro Del Vecchio; Devapratim Sarma; Nikhil Verma; Samuel C Colachis; Nicholas V Annetta; Jennifer L Collinger; Dario Farina; Douglas J Weber Journal: J Neurophysiol Date: 2021-11-17 Impact factor: 2.714