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Sensors 2017, 17(7), 1597;

A Novel Spatial Feature for the Identification of Motor Tasks Using High-Density Electromyography

Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona 08028, Spain
Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid 28029, Spain
Bioengineering Department, El Bosque University, Bogotá 110121, Colombia
Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan 81746-73441, Iran
Author to whom correspondence should be addressed.
Received: 21 April 2017 / Revised: 26 June 2017 / Accepted: 5 July 2017 / Published: 8 July 2017
(This article belongs to the Special Issue Wearable and Ambient Sensors for Healthcare and Wellness Applications)
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Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications. View Full-Text
Keywords: high-density electromyography; pattern recognition; myoelectric control; mean shift; prosthetics high-density electromyography; pattern recognition; myoelectric control; mean shift; prosthetics

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Jordanić, M.; Rojas-Martínez, M.; Mañanas, M.A.; Alonso, J.F.; Marateb, H.R. A Novel Spatial Feature for the Identification of Motor Tasks Using High-Density Electromyography. Sensors 2017, 17, 1597.

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