Identifying elderlies at risk of becoming more depressed with Internet-of-Things

Depression in the elderly is common and dangerous. Current methods to monitor elderly depression, however, are costly, time-consuming and inefficient. In this paper, we present a novel depression-monitoring system that infers an elderly’s changes in depression level based on his/her activity pattern...

Full description

Saved in:
Bibliographic Details
Main Authors: OU, Jiajue, LIANG, Huiguang, TAN, Hwee Xian
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
Subjects:
IoT
Online Access:https://ink.library.smu.edu.sg/sis_research/4096
https://ink.library.smu.edu.sg/context/sis_research/article/5099/viewcontent/Ou2018_Chapter_IdentifyingElderliesAtRiskOfBe.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-5099
record_format dspace
spelling sg-smu-ink.sis_research-50992018-12-27T08:15:44Z Identifying elderlies at risk of becoming more depressed with Internet-of-Things OU, Jiajue LIANG, Huiguang TAN, Hwee Xian Depression in the elderly is common and dangerous. Current methods to monitor elderly depression, however, are costly, time-consuming and inefficient. In this paper, we present a novel depression-monitoring system that infers an elderly’s changes in depression level based on his/her activity patterns, extracted from wireless sensor data. To do so, we build predictive models to learn the relationship between depression level changes and behaviors using historical data. We also deploy the system for a group of elderly, in their homes, and run the experiments for more than one year. Our experimental study gives encouraging results, suggesting that our IoT system is able to correctly identify >80% of the elderly at risk of becoming more depressed, with a very low false positive rate. 2018-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4096 info:doi/10.1007/978-3-319-92037-5_26 https://ink.library.smu.edu.sg/context/sis_research/article/5099/viewcontent/Ou2018_Chapter_IdentifyingElderliesAtRiskOfBe.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 Depression Elderly IoT Machine learning Gerontology Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Depression
Elderly
IoT
Machine learning
Gerontology
Software Engineering
spellingShingle Depression
Elderly
IoT
Machine learning
Gerontology
Software Engineering
OU, Jiajue
LIANG, Huiguang
TAN, Hwee Xian
Identifying elderlies at risk of becoming more depressed with Internet-of-Things
description Depression in the elderly is common and dangerous. Current methods to monitor elderly depression, however, are costly, time-consuming and inefficient. In this paper, we present a novel depression-monitoring system that infers an elderly’s changes in depression level based on his/her activity patterns, extracted from wireless sensor data. To do so, we build predictive models to learn the relationship between depression level changes and behaviors using historical data. We also deploy the system for a group of elderly, in their homes, and run the experiments for more than one year. Our experimental study gives encouraging results, suggesting that our IoT system is able to correctly identify >80% of the elderly at risk of becoming more depressed, with a very low false positive rate.
format text
author OU, Jiajue
LIANG, Huiguang
TAN, Hwee Xian
author_facet OU, Jiajue
LIANG, Huiguang
TAN, Hwee Xian
author_sort OU, Jiajue
title Identifying elderlies at risk of becoming more depressed with Internet-of-Things
title_short Identifying elderlies at risk of becoming more depressed with Internet-of-Things
title_full Identifying elderlies at risk of becoming more depressed with Internet-of-Things
title_fullStr Identifying elderlies at risk of becoming more depressed with Internet-of-Things
title_full_unstemmed Identifying elderlies at risk of becoming more depressed with Internet-of-Things
title_sort identifying elderlies at risk of becoming more depressed with internet-of-things
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/4096
https://ink.library.smu.edu.sg/context/sis_research/article/5099/viewcontent/Ou2018_Chapter_IdentifyingElderliesAtRiskOfBe.pdf
_version_ 1770574307362078720