Managing sensor systems for early detection of mild cognitive impairment in community elderly: Lessons learned and future work
The aging population is a pertinent issue faced by governments globally. One of the most common and costly health issues associated with the aging population is cognitive decline, leading up to dementia. In this paper, we describe a non-intrusive, continuous and scalable system for early detection o...
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sg-smu-ink.sis_research-51422018-10-05T02:52:42Z Managing sensor systems for early detection of mild cognitive impairment in community elderly: Lessons learned and future work NG, Boon Thai TAN, Hwee-Pink TAN, Hwee Xian The aging population is a pertinent issue faced by governments globally. One of the most common and costly health issues associated with the aging population is cognitive decline, leading up to dementia. In this paper, we describe a non-intrusive, continuous and scalable system for early detection of Mild Cognitive Impairment (MCI) in the elderly, which enables early medical interventions to be provided. We focus on the system design and feature extraction of the sensor system, to validate our hypothesis of the use of sensor systems for early detection of MCI. Lessons learned from deploying the sensor system is presented, together with the solutions that are implemented to improve system reliability 2017-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4139 https://ink.library.smu.edu.sg/context/sis_research/article/5142/viewcontent/IRC_SET_2017_paper_S1_5.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University sensor systems mild cognitive impairment early detection of MCI continuous system monitoring system reliability Gerontology Software Engineering |
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sensor systems mild cognitive impairment early detection of MCI continuous system monitoring system reliability Gerontology Software Engineering NG, Boon Thai TAN, Hwee-Pink TAN, Hwee Xian Managing sensor systems for early detection of mild cognitive impairment in community elderly: Lessons learned and future work |
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The aging population is a pertinent issue faced by governments globally. One of the most common and costly health issues associated with the aging population is cognitive decline, leading up to dementia. In this paper, we describe a non-intrusive, continuous and scalable system for early detection of Mild Cognitive Impairment (MCI) in the elderly, which enables early medical interventions to be provided. We focus on the system design and feature extraction of the sensor system, to validate our hypothesis of the use of sensor systems for early detection of MCI. Lessons learned from deploying the sensor system is presented, together with the solutions that are implemented to improve system reliability |
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text |
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NG, Boon Thai TAN, Hwee-Pink TAN, Hwee Xian |
author_facet |
NG, Boon Thai TAN, Hwee-Pink TAN, Hwee Xian |
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NG, Boon Thai |
title |
Managing sensor systems for early detection of mild cognitive impairment in community elderly: Lessons learned and future work |
title_short |
Managing sensor systems for early detection of mild cognitive impairment in community elderly: Lessons learned and future work |
title_full |
Managing sensor systems for early detection of mild cognitive impairment in community elderly: Lessons learned and future work |
title_fullStr |
Managing sensor systems for early detection of mild cognitive impairment in community elderly: Lessons learned and future work |
title_full_unstemmed |
Managing sensor systems for early detection of mild cognitive impairment in community elderly: Lessons learned and future work |
title_sort |
managing sensor systems for early detection of mild cognitive impairment in community elderly: lessons learned and future work |
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Institutional Knowledge at Singapore Management University |
publishDate |
2017 |
url |
https://ink.library.smu.edu.sg/sis_research/4139 https://ink.library.smu.edu.sg/context/sis_research/article/5142/viewcontent/IRC_SET_2017_paper_S1_5.pdf |
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1770574350610595840 |