Identification of independent biological sensors-electromyogram example.

Naik, GR; Kumar, DK; Palaniswami, M

Identification of independent biological sensors-electromyogram example.

Naik, GR

Kumar, DK Palaniswami, M

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Publication Type:: Conference Proceeding
Citation:: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2008, pp. 1112 - 1115
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Kumar, DK	en_US
dc.contributor.author	Palaniswami, M	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2008, pp. 1112 - 1115	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32004
dc.description.abstract	To ensure that no biological event that may be important is missed, redundancy of sensors is provided. While this is useful, there are shortcomings when there is need to separate the signals from different sources using blind source separation techniques. An example of such a situation is over-complete surface electromyogram (sEMG) recording. Techniques such as principal component analysis (PCA) and entropy measures are used to identify the suitable channels. The shortcomings in these are the need for prior estimation of the number of channels. This paper has used the determinant of the global matrix of the mixtures to determine the number of independent sources in a mixture. The results indicate that the technique is able to distinguish between dependent and independent channels and this may be applied for determining the number of independent sources. The applications of this include data reduction by identifying redundant data, and for pre-processing of the data prior to use of any data classification techniques.	en_US
dc.relation.ispartof	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference	en_US
dc.subject.mesh	Muscle, Skeletal	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Diagnosis, Computer-Assisted	en_US
dc.subject.mesh	Electromyography	en_US
dc.subject.mesh	Sensitivity and Specificity	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Electrodes	en_US
dc.subject.mesh	Transducers	en_US
dc.subject.mesh	Muscle Contraction	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Principal Component Analysis	en_US
dc.title	Identification of independent biological sensors-electromyogram example.	en_US
dc.type	Conference Proceeding
utslib.for	090302 Biomechanical Engineering	en_US
utslib.for	090399 Biomedical Engineering not elsewhere classified	en_US
utslib.for	090609 Signal Processing	en_US
dc.location.activity	Vancouver, BC
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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

To ensure that no biological event that may be important is missed, redundancy of sensors is provided. While this is useful, there are shortcomings when there is need to separate the signals from different sources using blind source separation techniques. An example of such a situation is over-complete surface electromyogram (sEMG) recording. Techniques such as principal component analysis (PCA) and entropy measures are used to identify the suitable channels. The shortcomings in these are the need for prior estimation of the number of channels. This paper has used the determinant of the global matrix of the mixtures to determine the number of independent sources in a mixture. The results indicate that the technique is able to distinguish between dependent and independent channels and this may be applied for determining the number of independent sources. The applications of this include data reduction by identifying redundant data, and for pre-processing of the data prior to use of any data classification techniques.

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http://hdl.handle.net/10453/32004