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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Theepop Chaitep | en_US |
dc.contributor.author | Jakarin Chawachat | en_US |
dc.date.accessioned | 2018-09-05T03:34:16Z | - |
dc.date.available | 2018-09-05T03:34:16Z | - |
dc.date.issued | 2017-11-03 | en_US |
dc.identifier.other | 2-s2.0-85039908690 | en_US |
dc.identifier.other | 10.1109/ECTICon.2017.8096203 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85039908690&origin=inward | en_US |
dc.identifier.uri | http://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.subject | Computer Science | en_US |
dc.subject | Engineering | en_US |
dc.subject | Neuroscience | en_US |
dc.subject | Physics and Astronomy | en_US |
dc.title | A 3-phase threshold algorithm for smartphone-based fall detection | en_US |
dc.type | Conference Proceeding | en_US |
article.title.sourcetitle | ECTI-CON 2017 - 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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