Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/57039
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dc.contributor.authorTheepop Chaitepen_US
dc.contributor.authorJakarin Chawachaten_US
dc.date.accessioned2018-09-05T03:34:16Z-
dc.date.available2018-09-05T03:34:16Z-
dc.date.issued2017-11-03en_US
dc.identifier.other2-s2.0-85039908690en_US
dc.identifier.other10.1109/ECTICon.2017.8096203en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85039908690&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/57039-
dc.description.abstract© 2017 IEEE. Falls are one of the prominent causes of injury in elderly. A fall detection could help reduce the health risk following the fall that would otherwise get overlooked. Many research studies mostly focus on distinguish a fall from other activities in daily life using smartphone. However, one major problem is a false positive created by a smartphone drop. In this paper, we propose a 3-phase threshold based fall detection algorithm for smartphone which can distinguish a fall from a smartphone drop. The experimental results show that our algorithm achieves a better performance than 2-phase threshold algorithm. Moreover, in smartphone drop cases, our algorithm has 72% specificity higher than 2-phase threshold algorithm which has 31% specificity.en_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.subjectNeuroscienceen_US
dc.subjectPhysics and Astronomyen_US
dc.titleA 3-phase threshold algorithm for smartphone-based fall detectionen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleECTI-CON 2017 - 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technologyen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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