Unobtrusive monitoring to detect depression for elderly with chronic illnesses

Mental health related disorders are common diseases, especially among the elder. Among the various mental health diseases, one potential threat to ageing-in-place is the risk of depression. In this paper, we propose a simple unobtrusive sensing system using passive infra-red motion sensors to monito...

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Main Authors: KIM, Jung-Yoon, LIU, Na, TAN, Hwee Xian, CHU, Chao-Hsien
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Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/sis_research/3773
https://ink.library.smu.edu.sg/context/sis_research/article/4775/viewcontent/07986964__1_.pdf
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spelling sg-smu-ink.sis_research-47752020-03-30T02:28:14Z Unobtrusive monitoring to detect depression for elderly with chronic illnesses KIM, Jung-Yoon LIU, Na TAN, Hwee Xian CHU, Chao-Hsien Mental health related disorders are common diseases, especially among the elder. Among the various mental health diseases, one potential threat to ageing-in-place is the risk of depression. In this paper, we propose a simple unobtrusive sensing system using passive infra-red motion sensors to monitor the activities of daily living of elderly, who are living alone. A feature extraction module comprising of three layers-states, events, and activities, and the corresponding algorithms are proposed to extract features. Four popular classification models-neural network, C4.5 decision tree, Bayesian network, and support vector machine are then applied to detect the severity of depression. We implement and test the algorithms on sensor data collected over three months from 20 elderly, each in different daily living conditions. Our evaluation shows that the proposed algorithms are effective in detecting both normal condition and mild depression with up to 96% accuracy, using neural network as the classification algorithm. The sensing system is non-intrusive and cost-effective, with the potential of use for long-term depression monitoring and detection of early symptoms of mental related disorders. This enables caregivers to provide timely interventions to elderly, who are at risk of depression. 2017-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3773 info:doi/10.1109/JSEN.2017.2729594 https://ink.library.smu.edu.sg/context/sis_research/article/4775/viewcontent/07986964__1_.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 Feature extraction depression detection smart homes unobtrusive monitoring sensor technologies Communication Technology and New Media Health Information Technology Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Feature extraction
depression detection
smart homes
unobtrusive monitoring
sensor technologies
Communication Technology and New Media
Health Information Technology
Software Engineering
spellingShingle Feature extraction
depression detection
smart homes
unobtrusive monitoring
sensor technologies
Communication Technology and New Media
Health Information Technology
Software Engineering
KIM, Jung-Yoon
LIU, Na
TAN, Hwee Xian
CHU, Chao-Hsien
Unobtrusive monitoring to detect depression for elderly with chronic illnesses
description Mental health related disorders are common diseases, especially among the elder. Among the various mental health diseases, one potential threat to ageing-in-place is the risk of depression. In this paper, we propose a simple unobtrusive sensing system using passive infra-red motion sensors to monitor the activities of daily living of elderly, who are living alone. A feature extraction module comprising of three layers-states, events, and activities, and the corresponding algorithms are proposed to extract features. Four popular classification models-neural network, C4.5 decision tree, Bayesian network, and support vector machine are then applied to detect the severity of depression. We implement and test the algorithms on sensor data collected over three months from 20 elderly, each in different daily living conditions. Our evaluation shows that the proposed algorithms are effective in detecting both normal condition and mild depression with up to 96% accuracy, using neural network as the classification algorithm. The sensing system is non-intrusive and cost-effective, with the potential of use for long-term depression monitoring and detection of early symptoms of mental related disorders. This enables caregivers to provide timely interventions to elderly, who are at risk of depression.
format text
author KIM, Jung-Yoon
LIU, Na
TAN, Hwee Xian
CHU, Chao-Hsien
author_facet KIM, Jung-Yoon
LIU, Na
TAN, Hwee Xian
CHU, Chao-Hsien
author_sort KIM, Jung-Yoon
title Unobtrusive monitoring to detect depression for elderly with chronic illnesses
title_short Unobtrusive monitoring to detect depression for elderly with chronic illnesses
title_full Unobtrusive monitoring to detect depression for elderly with chronic illnesses
title_fullStr Unobtrusive monitoring to detect depression for elderly with chronic illnesses
title_full_unstemmed Unobtrusive monitoring to detect depression for elderly with chronic illnesses
title_sort unobtrusive monitoring to detect depression for elderly with chronic illnesses
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/sis_research/3773
https://ink.library.smu.edu.sg/context/sis_research/article/4775/viewcontent/07986964__1_.pdf
_version_ 1770573728935051264