Electric powered wheelchair control using user-independent classification methods based on surface electromyography signals.

Iqbal, H; Zheng, J; Chai, R; Chandrasekaran, S

Electric powered wheelchair control using user-independent classification methods based on surface electromyography signals.

Iqbal, H Zheng, J Chai, R

Chandrasekaran, S

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Publisher:: SPRINGER HEIDELBERG
Publication Type:: Journal Article
Citation:: Med Biol Eng Comput, 2024, 62, (1), pp. 167-182
Issue Date:: 2024-01

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Field	Value	Language
dc.contributor.author	Iqbal, H
dc.contributor.author	Zheng, J
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024
dc.contributor.author	Chandrasekaran, S
dc.date.accessioned	2024-07-29T08:32:28Z
dc.date.available	2023-08-22
dc.date.available	2024-07-29T08:32:28Z
dc.date.issued	2024-01
dc.identifier.citation	Med Biol Eng Comput, 2024, 62, (1), pp. 167-182
dc.identifier.issn	0140-0118
dc.identifier.issn	1741-0444
dc.identifier.uri	http://hdl.handle.net/10453/179873
dc.description.abstract	Wheelchairs are one of the most popular assistive technology (AT) among individuals with motor impairments due to their comfort and mobility. People with finger problems may find it difficult to operate wheelchairs using the conventional joystick control method. Therefore, in this research study, a hand gesture-based control method is developed for operating an electric-powered wheelchair (EPW). This study selected a comfort-based hand position to determine the stop maneuver. An additional exploration was undertaken to investigate four gesture recognition methods: linear regression (LR), regularized linear regression (RLR), decision tree (DT), and multi-class support vector machine (MC-SVM). The first two methods, LR and RLR, have promising accuracy values of 94.85% and 95.88%, respectively, but each new user must be trained. To overcome this limitation, this study explored two user-independent classification methods: MC-SVM and DT. These methods effectively addressed the finger dependency issue and demonstrated remarkable success in recognizing gestures across different users. MC-SVM has about 99.05% of both precision and accuracy, and the DT has about 97.77% accuracy and precision. All six participants were successful in controlling the EPW without any collisions. According to the experimental results, the proposed approach has high accuracy and can address finger dependency issues.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	SPRINGER HEIDELBERG
dc.relation.ispartof	Med Biol Eng Comput
dc.relation.isbasedon	10.1007/s11517-023-02921-z
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering
dc.subject.classification	Biomedical Engineering
dc.subject.classification	4003 Biomedical engineering
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4611 Machine learning
dc.subject.mesh	Humans
dc.subject.mesh	Electromyography
dc.subject.mesh	Wheelchairs
dc.subject.mesh	Self-Help Devices
dc.subject.mesh	Equipment Design
dc.subject.mesh	Humans
dc.subject.mesh	Electromyography
dc.subject.mesh	Equipment Design
dc.subject.mesh	Self-Help Devices
dc.subject.mesh	Wheelchairs
dc.subject.mesh	Humans
dc.subject.mesh	Electromyography
dc.subject.mesh	Wheelchairs
dc.subject.mesh	Self-Help Devices
dc.subject.mesh	Equipment Design
dc.title	Electric powered wheelchair control using user-independent classification methods based on surface electromyography signals.
dc.type	Journal Article
utslib.citation.volume	62
utslib.location.activity	United States
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	recently_added	*
dc.date.updated	2024-07-29T08:32:22Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	62
utslib.citation.issue	1

Abstract:

Wheelchairs are one of the most popular assistive technology (AT) among individuals with motor impairments due to their comfort and mobility. People with finger problems may find it difficult to operate wheelchairs using the conventional joystick control method. Therefore, in this research study, a hand gesture-based control method is developed for operating an electric-powered wheelchair (EPW). This study selected a comfort-based hand position to determine the stop maneuver. An additional exploration was undertaken to investigate four gesture recognition methods: linear regression (LR), regularized linear regression (RLR), decision tree (DT), and multi-class support vector machine (MC-SVM). The first two methods, LR and RLR, have promising accuracy values of 94.85% and 95.88%, respectively, but each new user must be trained. To overcome this limitation, this study explored two user-independent classification methods: MC-SVM and DT. These methods effectively addressed the finger dependency issue and demonstrated remarkable success in recognizing gestures across different users. MC-SVM has about 99.05% of both precision and accuracy, and the DT has about 97.77% accuracy and precision. All six participants were successful in controlling the EPW without any collisions. According to the experimental results, the proposed approach has high accuracy and can address finger dependency issues.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/179873