Authors: Fang Wang; Mohammadreza Asghari Oskoei; Huosheng Hu
Addresses: Department of Automation, Shanghai University, Shanghai, 200072, China; Department of Electrical Engineering, Shanghai DianJi University, Shanghai 200240, China ' Department of Mathematics and Computer Science, University of Allameh Tabataba'i, Tehran, Iran ' School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Abstract: The paper investigates how multi-finger myoelectric signals could be used to control a virtual robotic prosthetic hand created by using robot operating system (ROS). Both off-line and online experiment phases are conducted by using ten electrodes and performing eight selected multi-finger motions on four healthy subjects. Classification accuracy and confusion matrix of eight time domain (TD)-features and two algorithms are compared during the off-line phase. Then the delay time and accuracy of online control of six selected TD-features and support vector machine (SVM) algorithm are presented with and without visual feedback from the virtual robotic prosthetic hand system. The experimental results show that different feature extraction principles have significant influence on online experiment performance when using SVM without visual feedback (SVMO), and the SVM with visual feedback (SVMW) has improved the online classification and recognition accuracy of eight multi-finger motions through all selected TD features.
Keywords: EMG control; pattern recognition; multiple-finger motions; virtual robotic hand; robot operating system.
International Journal of Modelling, Identification and Control, 2017 Vol.27 No.3, pp.181 - 190
Received: 21 Nov 2015
Accepted: 20 Apr 2016
Published online: 22 Apr 2017 *