Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2018
Abstract
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.
Keywords
Depression, Elderly, IoT, Machine learning
Discipline
Gerontology | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ITAP 2018: Proceedings of 4th International Conference on Human Aspects of IT for the Aged Population, Las Vegas, July 15-20
Volume
10927
First Page
348
Last Page
361
ISBN
9783319920375
Identifier
10.1007/978-3-319-92037-5_26
Publisher
Springer
City or Country
Cham
Citation
OU, Jiajue; LIANG, Huiguang; and TAN, Hwee Xian.
Identifying elderlies at risk of becoming more depressed with Internet-of-Things. (2018). ITAP 2018: Proceedings of 4th International Conference on Human Aspects of IT for the Aged Population, Las Vegas, July 15-20. 10927, 348-361.
Available at: https://ink.library.smu.edu.sg/sis_research/4096
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-319-92037-5_26