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

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Publication Type:: Conference Proceeding
Citation:: Conf Proc IEEE Eng Med Biol Soc, 2016, 2016 pp. 1696 - 1699
Issue Date:: 2016-08

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Field	Value	Language
dc.contributor.author	Khushaba, RN	en_US
dc.contributor.author	Al-Timemy, A	en_US
dc.contributor.author	Al-Ani, A	en_US
dc.contributor.author	Al-Jumaily, A	en_US
dc.date.accessioned	2017-03-24T05:03:18Z
dc.date.available	2017-03-24T05:03:18Z
dc.date.issued	2016-08	en_US
dc.identifier.citation	Conf Proc IEEE Eng Med Biol Soc, 2016, 2016 pp. 1696 - 1699	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/87536
dc.description.abstract	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	Conf Proc IEEE Eng Med Biol Soc	en_US
dc.relation.isbasedon	10.1109/EMBC.2016.7591042	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	en_US
utslib.location.activity	United States	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 Elec, Mech and Mechatronic Systems
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - GEVI - Green Energy and Vehicle Innovations
utslib.copyright.status	open_access
pubs.declined	2017-03-24T16:03:18.820+1100
pubs.deleted	2017-03-24T16:03:18.820+1100
pubs.publication-status	Published	en_US
pubs.volume	2016	en_US

Abstract:

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.

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http://hdl.handle.net/10453/87536