Publication Type

Journal Article

Publication Date

9-2017

Abstract

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 and test the algorithms on sensor data collected over three months from 20 elderly, each in different daily living conditions. Our evaluation shows that the proposed algorithms are effective in detecting both normal condition and mild depression with up to 96% accuracy, using neural network as the classification algorithm. The sensing system is non-intrusive and cost-effective, with the potential of use for long-term depression monitoring and detection of early symptoms of mental related disorders. This enables caregivers to provide timely interventions to elderly, who are at risk of depression.

Keywords

Feature extraction, depression detection, smart homes, unobtrusive monitoring, sensor technologies

Discipline

Communication Technology and New Media | Health Information Technology

Publication

IEEE Sensors Journal

Volume

17

Issue

17

First Page

5694

Last Page

5704

ISSN

1530-437X

Identifier

10.1109/JSEN.2017.2729594

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org./10.1109/JSEN.2017.2729594

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