Myoelectric feature extraction using temporal-spatial descriptors for multifunction prosthetic hand control

Khushaba, RN; Al-Timemy, A; Al-Ani, A; Al-Jumaily, A

Myoelectric feature extraction using temporal-spatial descriptors for multifunction prosthetic hand control

Khushaba, RN

Al-Timemy, A Al-Ani, A

Al-Jumaily, A

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016, 2016-October pp. 1696 - 1699
Issue Date:: 2016-10-13

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted ManuscriptAdobe PDF (827.01 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Al-Timemy, A	en_US
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2016-10-13	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016, 2016-October pp. 1696 - 1699	en_US
dc.identifier.isbn	9781457702204	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/106352
dc.description.abstract	© 2016 IEEE. We tackle the challenging problem of myoelectric prosthesis control with an improved feature extraction algorithm. The proposed algorithm correlates a set of spectral moments and their nonlinearly mapped version across the temporal and spatial domains to form accurate descriptors of muscular activity. The main processing step involves the extraction of the Electromyogram (EMG) signal power spectrum characteristics directly from the time-domain for each analysis window, a step to preserve the computational power required for the construction of spectral features. The subsequent analyses involve computing 1) the correlation between the time-domain descriptors extracted from each analysis window and a nonlinearly mapped version of it across the same EMG channel; representing the temporal evolution of the EMG signals, and 2) the correlation between the descriptors extracted from differences of all possible combinations of channels and a nonlinearly mapped version of them, focusing on how the EMG signals from different channels correlates with each other. The proposed Temporal-Spatial Descriptors (TSDs) are validated on EMG data collected from six transradial amputees performing 11 classes of finger movements. Classification results showed significant reductions (at least 8%) in classification error rates compared to other methods.	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.2016.7591042	en_US
dc.subject.mesh	Hand	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Prostheses and Implants	en_US
dc.subject.mesh	Movement	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Amputees	en_US
dc.subject.mesh	Spatial Analysis	en_US
dc.title	Myoelectric feature extraction using temporal-spatial descriptors for multifunction prosthetic hand control	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-October	en_US
utslib.for	090399 Biomedical Engineering not elsewhere classified	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	open_access
pubs.publication-status	Published	en_US
pubs.volume	2016-October	en_US

Abstract:

© 2016 IEEE. We tackle the challenging problem of myoelectric prosthesis control with an improved feature extraction algorithm. The proposed algorithm correlates a set of spectral moments and their nonlinearly mapped version across the temporal and spatial domains to form accurate descriptors of muscular activity. The main processing step involves the extraction of the Electromyogram (EMG) signal power spectrum characteristics directly from the time-domain for each analysis window, a step to preserve the computational power required for the construction of spectral features. The subsequent analyses involve computing 1) the correlation between the time-domain descriptors extracted from each analysis window and a nonlinearly mapped version of it across the same EMG channel; representing the temporal evolution of the EMG signals, and 2) the correlation between the descriptors extracted from differences of all possible combinations of channels and a nonlinearly mapped version of them, focusing on how the EMG signals from different channels correlates with each other. The proposed Temporal-Spatial Descriptors (TSDs) are validated on EMG data collected from six transradial amputees performing 11 classes of finger movements. Classification results showed significant reductions (at least 8%) in classification error rates compared to other methods.

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

http://hdl.handle.net/10453/106352