Time-dependent spectral features for limb position invariant myoelectric pattern recognition

Khushaba, RN; Shi, L; Kodagoda, S

Time-dependent spectral features for limb position invariant myoelectric pattern recognition

Khushaba, RN

Shi, L

Kodagoda, S

Permalink

Publication Type:: Conference Proceeding
Citation:: 2012 International Symposium on Communications and Information Technologies, ISCIT 2012, 2012, pp. 1015 - 1020
Issue Date:: 2012-12-01

Closed Access

	Filename	Description	Size
	2011007262OK.pdf		3.07 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Shi, L https://orcid.org/0000-0002-5215-025X	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	2012 International Symposium on Communications and Information Technologies, ISCIT 2012, 2012, pp. 1015 - 1020	en_US
dc.identifier.isbn	9781467311571	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22396
dc.description.abstract	Recent studies on the myoelectric control of powered prosthetics revealed several factors that affect its clinical performance. One of the important factors is the variation in the limb position associated with normal use which can have a substantial impact on the robustness of Electromyogram (EMG) pattern recognition. To solve this problem, we propose in this paper a new feature extraction algorithm based on set of spectral moments that extracts the relevant information about the EMG power spectrum in an accurate and efficient manner. The main goal is to rely on effective knowledge discovery and pattern recognition methods to discover the neural information embedded in the EMG signals regardless of the limb position. Specifically, the proposed features define descriptive qualities for the general time domain-based characterization of the EMG spectral amplitude, spectral sparsity, and irregularity factor by the application of mathematical-statistical methods which also include frequency consideration. The performance of the proposed spectral moments is tested on EMG data collected from eight subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that training the classifier on the EMG moments collected from multiple positions and testing on completely unseen positions can achieve significant reduction in the classification error rates of upon ≈10% on average across all subjects and limb positions. © 2012 IEEE.	en_US
dc.relation.ispartof	2012 International Symposium on Communications and Information Technologies, ISCIT 2012	en_US
dc.relation.isbasedon	10.1109/ISCIT.2012.6380840	en_US
dc.title	Time-dependent spectral features for limb position invariant myoelectric pattern recognition	en_US
dc.type	Conference Proceeding
utslib.for	090602 Control Systems, Robotics and Automation	en_US
dc.location.activity	Surfers Paradise, Gold Coast, Australia	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 - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Recent studies on the myoelectric control of powered prosthetics revealed several factors that affect its clinical performance. One of the important factors is the variation in the limb position associated with normal use which can have a substantial impact on the robustness of Electromyogram (EMG) pattern recognition. To solve this problem, we propose in this paper a new feature extraction algorithm based on set of spectral moments that extracts the relevant information about the EMG power spectrum in an accurate and efficient manner. The main goal is to rely on effective knowledge discovery and pattern recognition methods to discover the neural information embedded in the EMG signals regardless of the limb position. Specifically, the proposed features define descriptive qualities for the general time domain-based characterization of the EMG spectral amplitude, spectral sparsity, and irregularity factor by the application of mathematical-statistical methods which also include frequency consideration. The performance of the proposed spectral moments is tested on EMG data collected from eight subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that training the classifier on the EMG moments collected from multiple positions and testing on completely unseen positions can achieve significant reduction in the classification error rates of upon ≈10% on average across all subjects and limb positions. © 2012 IEEE.

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

http://hdl.handle.net/10453/22396