Spatio-Temporal Inertial Measurements Feature Extraction Improves Hand Movement Pattern Recognition without Electromyography

Khushaba, RN; Krasoulis, A; Al-Jumaily, A; Nazarpour, K

Spatio-Temporal Inertial Measurements Feature Extraction Improves Hand Movement Pattern Recognition without Electromyography

Khushaba, RN

Krasoulis, A Al-Jumaily, A

Nazarpour, K

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, 2018-July pp. 2108 - 2111
Issue Date:: 2018-10-26

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Field	Value	Language
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Krasoulis, A	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Nazarpour, K	en_US
dc.date.issued	2018-10-26	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2018, 2018-July pp. 2108 - 2111	en_US
dc.identifier.isbn	9781538636466	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133201
dc.description.abstract	© 2018 IEEE. Recent studies indicate the limited clinical acceptance of myoelectric prostheses, as upper extremity amputees need improved functionality and more intuitive, effective, and coordinated control of their artificial limbs. Rather than exclusively classifying the electromyogram (EMG) signals, it has been shown that inertial measurements (IMs) can form an excellent complementary signal to the EMG signals to improve the prosthetic control robustness. We present an investigation into the possibility of replacing, rather than complementing, the EMG signals with IMs. We hypothesize that the enhancements achieved by the combined use of the EMG and IM signals may not be significantly different from that achieved by the use of Magnetometer (MAG) or Accelerometer (ACC) signals only, when the temporal and spatial information aspects are considered. A large dataset comprising recordings with 20 ablebodied and two amputee participants, executing 40 movements, was collected. A systematic performance comparison across a number of feature extraction methods was carried out to test our hypothesis. Results suggest that, individually, each of the ACC and MMG signals can form an excellent and potentially independent source of control signal for upper-limb prostheses, with an average classification accuracy of \approx 93% across all subjects. This study suggests the feasibility of moving from surface EMG to IM signals as a main source for upper-limb prosthetic control in real-life applications.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2018.8512638	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.mesh	Hand	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Artificial Limbs	en_US
dc.subject.mesh	Movement	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.subject.mesh	Amputees	en_US
dc.title	Spatio-Temporal Inertial Measurements Feature Extraction Improves Hand Movement Pattern Recognition without Electromyography	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-July	en_US
utslib.for	0903 Biomedical Engineering	en_US
pubs.embargo.period	Not known	en_US
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-July	en_US

Abstract:

© 2018 IEEE. Recent studies indicate the limited clinical acceptance of myoelectric prostheses, as upper extremity amputees need improved functionality and more intuitive, effective, and coordinated control of their artificial limbs. Rather than exclusively classifying the electromyogram (EMG) signals, it has been shown that inertial measurements (IMs) can form an excellent complementary signal to the EMG signals to improve the prosthetic control robustness. We present an investigation into the possibility of replacing, rather than complementing, the EMG signals with IMs. We hypothesize that the enhancements achieved by the combined use of the EMG and IM signals may not be significantly different from that achieved by the use of Magnetometer (MAG) or Accelerometer (ACC) signals only, when the temporal and spatial information aspects are considered. A large dataset comprising recordings with 20 ablebodied and two amputee participants, executing 40 movements, was collected. A systematic performance comparison across a number of feature extraction methods was carried out to test our hypothesis. Results suggest that, individually, each of the ACC and MMG signals can form an excellent and potentially independent source of control signal for upper-limb prostheses, with an average classification accuracy of \approx 93% across all subjects. This study suggests the feasibility of moving from surface EMG to IM signals as a main source for upper-limb prosthetic control in real-life applications.

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http://hdl.handle.net/10453/133201