Capacitive Sensor to Detect Fallen Humans in Conditions of Low Visibility

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dc.contributor.author Moulton, Bruce en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-10-12T03:33:57Z
dc.date.available 2012-10-12T03:33:57Z
dc.date.issued 2011 en_US
dc.identifier 2011001449 en_US
dc.identifier.citation Moulton Bruce 2011, 'Capacitive Sensor to Detect Fallen Humans in Conditions of Low Visibility', Advanced Institute of Convergence IT, vol. 6, no. 9, pp. 1-8. en_US
dc.identifier.issn 1975-9320 en_US
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/18358
dc.description.abstract This paper examines the potential for a capacitive sensor to be used as part of a system to detect fallen humans at very close range. Previous research suggests that a robotic system incorporating a low cost capacitive sensor could potentially distinguish between different materials. The work reported in this paper stemmed from an attempt to determine the true extent to which such a system might reliably differentiate between fallen humans and other objects. The work is motivated by the fact that there are several different emergency circumstances in which such a system might save lives if it could reliably detect immobile humans. These scenarios include situations where older people have fallen and are unable to move or raise an alert, and circumstances where people have been overcome by smoke in a burning building. Current sensing systems are typically unsuitable in conditions of low visibility such as smoke filled rooms. This analysis focused specifically on the potential for a robot equipped with a capacitive sensing system to identify an immobile human in a low visibility emergency scenario. It is concluded that further work would be required to determine whether this type of capacitive sensing system is genuinely suitable for this task. en_US
dc.language English en_US
dc.publisher Advanced Institute of Convergence IT en_US
dc.title Capacitive Sensor to Detect Fallen Humans in Conditions of Low Visibility en_US
dc.parent Journal of Convergence Information Technology en_US
dc.journal.volume 6 en_US
dc.journal.number 9 en_US
dc.publocation United States en_US
dc.identifier.startpage 1 en_US
dc.identifier.endpage 8 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 090300 en_US
dc.personcode 010755 en_US
dc.percentage 100 en_US
dc.classification.name Biomedical Engineering en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Capacitive Sensor, Material-type Classification, Robot, Emergency, Fall, Fire, Health Alert System en_US
dc.staffid en_US
dc.staffid 010755 en_US


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