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Medicine and Health Sciences

Singapore Management University

Depression detection

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Full-Text Articles in Physical Sciences and Mathematics

Stressmon: Large Scale Detection Of Stress And Depression In Campus Environment Using Passive Coarse-Grained Location Data, Camellia Zakaria Jul 2019

Stressmon: Large Scale Detection Of Stress And Depression In Campus Environment Using Passive Coarse-Grained Location Data, Camellia Zakaria

Dissertations and Theses Collection (Open Access)

The rising mental health illnesses of severe stress and depression is of increasing concern worldwide. Often associated by similarities in symptoms, severe stress can take a toll on a person’s productivity and result in depression if the stress is left unmanaged. Unfortunately, depression can occur without any feelings of stress. With depression growing as a leading cause of disability in economic productivity, there has been a sharp rise in mental health initiatives to improve stress and depression management. To offer such services conveniently and discreetly, recent efforts have focused on using mobile technologies. However, these initiatives usually require users to …


Unobtrusive Monitoring To Detect Depression For Elderly With Chronic Illnesses, Jung-Yoon Kim, Na Liu, Hwee Xian Tan, Chao-Hsien Chu Sep 2017

Unobtrusive Monitoring To Detect Depression For Elderly With Chronic Illnesses, Jung-Yoon Kim, Na Liu, Hwee Xian Tan, Chao-Hsien Chu

Research Collection School Of Computing and Information Systems

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 …