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Title: Pattern classification of hand movements using time domain features of electromyography
Authors: Robinson, Carl Peter
Li, Baihua
Meng, Qinggang
Pain, Matthew T.G.
Keywords: Electromyography
Myoelectric control
Time domain features
Machine learning
Issue Date: 2017
Publisher: © Association for Computing Machinery (ACM)
Citation: ROBINSON, C.P. ...et al., 2017. Pattern classification of hand movements using time domain features of electromyography. Presented at the 4th International Conference on Movement Computing (MOCO '17), London, 28-30th June, Article no 27.
Abstract: Myoelectric control of prostheses is a long-established technique, using surface electromyography (sEMG) to detect the electrical signals of muscle activity and perform subsequent mechanical actions. Despite several decades’ research, robust, responsive and intuitive control schemes remain elusive. Current commercial hardware advances offer a variety of movements but the control systems are unnatural, using sequential switching methods triggered by specific sEMG signals. However, recent research with pattern recognition and simultaneous and proportional control shows good promise for natural myoelectric control. This paper investigates several sEMG time domain features using a series of hand movements performed by 11 subjects, taken from a benchmark database, to determine if optimal classification accuracy is dependent on feature set size. The features were extracted from the data using a sliding window process and applied to five machine learning classifiers, of which Random Forest consistently performed best. Results suggest a few simple features such as Root Mean Square and Waveform Length achieve comparable performance to using the entire feature set, when identifying the hand movements, although further work is required for feature optimisation.
Description: Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. The definitive Version of Record was published in Communications of the ACM: https://doi.org/10.1145/3077981.3078031
Version: Published
DOI: 10.1145/3077981.3078031
URI: https://dspace.lboro.ac.uk/2134/26360
Publisher Link: https://doi.org/10.1145/3077981.3078031
ISBN: 9781450352093
Appears in Collections:Conference Papers and Presentations (Computer Science)
Conference Papers and Presentations (Sport, Exercise and Health Sciences)

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