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

• Main Authors: OU, Jiajue, LIANG, Huiguang, TAN, Hwee Xian
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Depression; Elderly; IoT; Machine learning; Gerontology; Software Engineering
• 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
• Institution: Singapore Management University

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...