A Position Weight Matrix Feature Extraction Algorithm Improves Hand Gesture Recognition.

Chahid, A; Khushaba, R; Al-Jumaily, A; Laleg-Kirati, T-M

A Position Weight Matrix Feature Extraction Algorithm Improves Hand Gesture Recognition.

Chahid, A Khushaba, R Al-Jumaily, A Laleg-Kirati, T-M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 2020, pp. 5765-5768
Issue Date:: 2020-07

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Field	Value	Language
dc.contributor.author	Chahid, A
dc.contributor.author	Khushaba, R
dc.contributor.author	Al-Jumaily, A
dc.contributor.author	Laleg-Kirati, T-M
dc.date	2020-07-20
dc.date.accessioned	2021-06-16T07:04:10Z
dc.date.available	2021-06-16T07:04:10Z
dc.date.issued	2020-07
dc.identifier.citation	Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 2020, pp. 5765-5768
dc.identifier.isbn	9781728119908
dc.identifier.issn	2375-7477
dc.identifier.issn	2694-0604
dc.identifier.uri	http://hdl.handle.net/10453/149557
dc.description.abstract	Recent advances in the biomedical field have generated a massive amount of data and records (signals) that are collected for diagnosis purposes. The correct interpretation and understanding of these signals present a big challenge for digital health vision. In this work, Quantization-based position Weight Matrix (QuPWM) feature extraction method for multiclass classification is proposed to improve the interpretation of biomedical signals. This method is validated on surface Electromyogram (sEMG) signals recognition for eight different hand gestures. The used CapgMyo dataset consists of high-density sEMG signals across 128 channels acquired from 9 intact subjects. Our pilot results show that an accuracy of up to 83% can be achieved for some subjects using a support vector machine classifier, and an average accuracy of 75% has been reached for all studied subjects using the CapgMyo dataset. The proposed method shows a good potential in extracting relevant features from different biomedical signals such as Electroencephalogram (EEG) and Magnetoencephalogram (MEG) signals.
dc.format	Print
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
dc.relation.ispartof	2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society
dc.relation.isbasedon	10.1109/embc44109.2020.9176097
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.mesh	Electromyography
dc.subject.mesh	Gestures
dc.subject.mesh	Algorithms
dc.subject.mesh	Pattern Recognition, Automated
dc.subject.mesh	Position-Specific Scoring Matrices
dc.subject.mesh	Algorithms
dc.subject.mesh	Electromyography
dc.subject.mesh	Gestures
dc.subject.mesh	Pattern Recognition, Automated
dc.subject.mesh	Position-Specific Scoring Matrices
dc.title	A Position Weight Matrix Feature Extraction Algorithm Improves Hand Gesture Recognition.
dc.type	Conference Proceeding
utslib.citation.volume	2020
utslib.location.activity	United States
utslib.copyright.status	closed_access	*
dc.date.updated	2021-06-16T07:04:09Z
pubs.finish-date	2020-07-24
pubs.publication-status	Published
pubs.start-date	2020-07-20
pubs.volume	2020

Abstract:

Recent advances in the biomedical field have generated a massive amount of data and records (signals) that are collected for diagnosis purposes. The correct interpretation and understanding of these signals present a big challenge for digital health vision. In this work, Quantization-based position Weight Matrix (QuPWM) feature extraction method for multiclass classification is proposed to improve the interpretation of biomedical signals. This method is validated on surface Electromyogram (sEMG) signals recognition for eight different hand gestures. The used CapgMyo dataset consists of high-density sEMG signals across 128 channels acquired from 9 intact subjects. Our pilot results show that an accuracy of up to 83% can be achieved for some subjects using a support vector machine classifier, and an average accuracy of 75% has been reached for all studied subjects using the CapgMyo dataset. The proposed method shows a good potential in extracting relevant features from different biomedical signals such as Electroencephalogram (EEG) and Magnetoencephalogram (MEG) signals.

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http://hdl.handle.net/10453/149557