Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor
© 2018 Bunthit Watanapa et al. This paper proposes a fall severity analytic and post-fall intelligence system with three interdependent modules. Module I is the analysis of fall severity based on factors extracted in the phases of during and after fall which include innovative measures of the sequen...
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th-mahidol.456862019-08-23T18:10:24Z Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor Bunthit Watanapa Orasa Patsadu Piyapat Dajpratham Chakarida Nukoolkit Rajamangala University of Technology system Faculty of Medicine, Siriraj Hospital, Mahidol University King Mongkut s University of Technology Thonburi Computer Science Engineering © 2018 Bunthit Watanapa et al. This paper proposes a fall severity analytic and post-fall intelligence system with three interdependent modules. Module I is the analysis of fall severity based on factors extracted in the phases of during and after fall which include innovative measures of the sequence of body impact, level of impact, and duration of motionlessness. Module II is a timely autonomic notification to relevant persons with context-dependent fall severity alert via electronic communication channels (e.g., smartphone, tablet, or smart TV set). Lastly, Module III is the diagnostic support for caregivers and doctors to have information for making a well-informed decision of first aid or postcure with the chronologically traceable intelligence of information and knowledge found in Modules I and II. The system shall be beneficial to caregivers or doctors, in giving first aid/diagnosis/treatment to the subject, especially, in cases where the subject has lost consciousness and is unable to respond. 2019-08-23T10:59:27Z 2019-08-23T10:59:27Z 2018-01-01 Article Applied Computational Intelligence and Soft Computing. Vol.2018, (2018) 10.1155/2018/5434897 16879732 16879724 2-s2.0-85049104233 https://repository.li.mahidol.ac.th/handle/123456789/45686 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049104233&origin=inward |
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Computer Science Engineering Bunthit Watanapa Orasa Patsadu Piyapat Dajpratham Chakarida Nukoolkit Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor |
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© 2018 Bunthit Watanapa et al. This paper proposes a fall severity analytic and post-fall intelligence system with three interdependent modules. Module I is the analysis of fall severity based on factors extracted in the phases of during and after fall which include innovative measures of the sequence of body impact, level of impact, and duration of motionlessness. Module II is a timely autonomic notification to relevant persons with context-dependent fall severity alert via electronic communication channels (e.g., smartphone, tablet, or smart TV set). Lastly, Module III is the diagnostic support for caregivers and doctors to have information for making a well-informed decision of first aid or postcure with the chronologically traceable intelligence of information and knowledge found in Modules I and II. The system shall be beneficial to caregivers or doctors, in giving first aid/diagnosis/treatment to the subject, especially, in cases where the subject has lost consciousness and is unable to respond. |
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Rajamangala University of Technology system |
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Rajamangala University of Technology system Bunthit Watanapa Orasa Patsadu Piyapat Dajpratham Chakarida Nukoolkit |
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Article |
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Bunthit Watanapa Orasa Patsadu Piyapat Dajpratham Chakarida Nukoolkit |
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Bunthit Watanapa |
title |
Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor |
title_short |
Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor |
title_full |
Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor |
title_fullStr |
Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor |
title_full_unstemmed |
Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor |
title_sort |
post-fall intelligence supporting fall severity diagnosis using kinect sensor |
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2019 |
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https://repository.li.mahidol.ac.th/handle/123456789/45686 |
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1763487742333812736 |