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dc.contributor.authorSahayadhas, Arun
dc.contributor.authorSundaraj, Kenneth, Prof. Dr.
dc.contributor.authorMurugappan, M
dc.date.accessioned2014-04-01T07:05:58Z
dc.date.available2014-04-01T07:05:58Z
dc.date.issued2012
dc.identifier.citationSensors 2012, vol. 12(12), pages 16937-16953en_US
dc.identifier.issn1424-8220
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/33320
dc.identifier.urihttp://www.mdpi.com/1424-8220/12/12/16937
dc.descriptionLink to publisher's homepage at http://www.mdpi.com/en_US
dc.description.abstractIn recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.en_US
dc.language.isoenen_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.subjectDriver drowsiness detectionen_US
dc.subjectTransportation safetyen_US
dc.subjectHybrid measuresen_US
dc.subjectDriver fatigueen_US
dc.subjectArtificial intelligence techniquesen_US
dc.subjectSensor fusionen_US
dc.titleDetecting driver drowsiness based on sensors-a reviewen_US
dc.typeArticleen_US
dc.identifier.urlhttp://dx.doi.org/10.3390/s121216937
dc.contributor.urlarurun@gmail.comen_US
dc.contributor.urlkenneth@unimap.edu.myen_US
dc.contributor.urlmurugappan@unimap.edu.myen_US


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