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

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1007/978-3-319-92037-5_26

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