Applying Neural network Analysis on Heart Rate variability Data to Assess Driver Fatigue

Patel, M; Lal, S; Kavanagh, D; Rossiter, P

Applying Neural network Analysis on Heart Rate variability Data to Assess Driver Fatigue

Patel, M Lal, S Kavanagh, D Rossiter, P

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Patel Miteshkumar et al. 2011, 'Applying Neural network Analysis on Heart Rate variability Data to Assess Driver Fatigue', ELSEVIER, vol. 38, no. 6, pp. 7235-7242.
Issue Date:: 2011

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Field	Value	Language
dc.contributor.author	Patel, M	en_US
dc.contributor.author	Lal, S	en_US
dc.contributor.author	Kavanagh, D	en_US
dc.contributor.author	Rossiter, P	en_US
dc.date.accessioned	2012-02-02T04:17:01Z
dc.date.available	2012-02-02T04:17:01Z
dc.date.issued	2011	en_US
dc.identifier	2010003262	en_US
dc.identifier.citation	Patel Miteshkumar et al. 2011, 'Applying Neural network Analysis on Heart Rate variability Data to Assess Driver Fatigue', ELSEVIER, vol. 38, no. 6, pp. 7235-7242.	en_US
dc.identifier.issn	0957-4174	en_US
dc.identifier.other	C1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14508
dc.description.abstract	Long duration driving is a significant cause of fatigue related accidents on motorways. Fatigue caused by driving for extended hours can acutely impair driverâ¿¿s alertness and performance. This papers presents an artificial intelligence based system which could detect early onset of fatigue in drivers using heart rate variability (HRV) as the human physiological measure. The detection performance of neural network was tested using a set of electrocardiogram (ECG) data recorded under laboratory conditions. The neural network gave an accuracy of 90%. This HRV based fatigue detection technique can be used as a fatigue countermeasure.	en_US
dc.publisher	ELSEVIER	en_US
dc.relation.ispartof	Verified OK	en_US
dc.relation.ispartof	Expert Systems with Applications	en_US
dc.relation.isbasedon	http://dx.doi.org/10.1016/j.eswa.2010.12.028	en_US
dc.subject	Heart rate variability, Neural network, Driver fatigue, Spectral analysis	en_US
dc.subject.classification	Applied Mathematics	en_US
dc.title	Applying Neural network Analysis on Heart Rate variability Data to Assess Driver Fatigue	en_US
dc.type	Journal article
utslib.citation.volume	38	en_US
utslib.citation.number	6	en_US
utslib.location	U.S.A	en_US
utslib.citation.startpage	7235	en_US
utslib.citation.endpage	7242	en_US
utslib.identifier.org	FEIT.Faculty of Engineering & Information Technology	en_US
utslib.for	010200	en_US
utslib.percentage	000100	en_US
utslib.copyright.status	closed_access

Abstract:

Long duration driving is a significant cause of fatigue related accidents on motorways. Fatigue caused by driving for extended hours can acutely impair driverâ¿¿s alertness and performance. This papers presents an artificial intelligence based system which could detect early onset of fatigue in drivers using heart rate variability (HRV) as the human physiological measure. The detection performance of neural network was tested using a set of electrocardiogram (ECG) data recorded under laboratory conditions. The neural network gave an accuracy of 90%. This HRV based fatigue detection technique can be used as a fatigue countermeasure.

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

http://hdl.handle.net/10453/14508