Study on interaction between temporal and spatial information in classification of EMG signals in myoelectric prostheses
Menon, Radhika and Di Caterina, Gaetano and Lakany, Heba and Petropoulakis, Lykourgos and Conway, Bernard A. and Soraghan, John J. (2017) Study on interaction between temporal and spatial information in classification of EMG signals in myoelectric prostheses. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (10). pp. 1832-1842. ISSN 1534-4320 (https://doi.org/10.1109/TNSRE.2017.2687761)
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Abstract
Advanced forearm prosthetic devices employ classifiers to recognize different electromyography (EMG) signal patterns, in order to identify the user's intended motion gesture. The classification accuracy is one of the main determinants of real-time controllability of a prosthetic limb and hence the necessity to achieve as high an accuracy as possible. In this paper, we study the effects of the temporal and spatial information provided to the classifier on its offline performance and analyze their interdependencies. EMG data associated with seven practical hand gestures were recorded from partial-hand and trans-radial amputee volunteers as well as able-bodied volunteers. An extensive investigation was conducted to study the effect of analysis window length, window overlap a nd the number of electrode channels on the classification accuracy as well as their interactions. Our main discoveries are that the effect of analysis window length on classification accuracy is practically independent of the number of electrodes for all participant groups; window overlap has no direct influence on classifier performance, irrespective of the window length, number of channels or limb condition; the type of limb deficiency and the existing channel count influence the reduction in classification error achieved by adding more number of channels; partial-hand amputees outperform trans-radial amputees, with classification accuracies of only 11.3 % below values achieved by able-bodied volunteers.
ORCID iDs
Menon, Radhika ORCID: https://orcid.org/0000-0001-9640-2052, Di Caterina, Gaetano ORCID: https://orcid.org/0000-0002-7256-0897, Lakany, Heba ORCID: https://orcid.org/0000-0003-3079-0392, Petropoulakis, Lykourgos ORCID: https://orcid.org/0000-0003-3230-9670, Conway, Bernard A. ORCID: https://orcid.org/0000-0002-0069-0131 and Soraghan, John J. ORCID: https://orcid.org/0000-0003-4418-7391;-
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Item type: Article ID code: 59977 Dates: DateEvent31 October 2017Published19 April 2017Published Online27 February 2017AcceptedNotes: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Faculty of Engineering > Biomedical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 27 Feb 2017 14:35 Last modified: 04 Dec 2024 01:18 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/59977