Uncovering patterns of forearm muscle activity using multi-channel mechanomyography |
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| Authors: | Natasha Alves Tom Chau |
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| Institution: | 1. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. Department of Biomedical Engineering, Faculty of Engineering and Technology, University of Ilorin, P. M. B. 1515 Ilorin, Nigeria;3. Department of Rehabilitation Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia;4. Clinical Exercise and Rehabilitation Unit, Discipline of Exercise and Sports Sciences, Faculty of Health Sciences, The University of Sydney, Sydney, 2006 NSW, Australia;1. Henry Ford Health System, 2799 West Grand Blvd. CFP-6, Detroit, MI 48202, USA;2. Spine and Scoliosis Specialists, 14505 University Point Pl, Tampa, FL 33613, USA;1. Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia;2. Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Iraq;3. Center for Robotics and Neural Systems (CRNS), Plymouth University, UK;4. School of Electrical and Electronic Engineering, Newcastle University, Newcastle-upon-Tyne NE1 7RU, UK;5. Institute of Neuroscience, Newcastle University, Newcastle-upon-Tyne NE2 4HH, UK |
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| Abstract: | A coordinated activation of distal forearm muscles allows the hand and fingers to be shaped during movement and grasp. However, little is known about how the muscle activation patterns are reflected in multi-channel mechanomyogram (MMG) signals. The purpose of this study is to determine if multi-site MMG signals exhibit distinctive patterns of forearm muscle activity. MMG signals were recorded from forearm muscle sites of nine able-bodied participants during hand movement. By using 14 features selected by a genetic algorithm and classified by a linear discriminant analysis classifier (LDA), we show that MMG patterns are specific and consistent enough to identify 7 ± 1 hand movements with an accuracy of 90 ± 4%. MMG-based movement recognition required a minimum of three recording sites. Further, by classifying five classes of contraction patterns with 98 ± 3% accuracy from MMG signals recorded from the residual limb of an amputee participant, we demonstrate that MMG shows pattern-specificity even in the absence of typical musculature. Multi-site monitoring of the RMS of MMG signals is suggested as a method of estimating the relative contributions of muscles to motor tasks. The patterns in MMG facilitate our understanding of the mechanical activity of muscles during movement. |
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