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IOT structured Long-term Wearable Social Sensing for Mental Wellbeing

Lookup NU author(s): Professor Bin Gao, Dr Long Jiang, Dr Wai Lok Woo


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IEEE Long-term wellbeing monitoring is an underlying theme for evaluating health status by collecting physiological signs through behavioral traits. In alignment with internet of things (IoT), non-intrusive and trustworthy wearable social sensing technology holds a potential way for researchers to find and establish the interrelationships between unobtrusive social cues and physical mental health (PMH). This paper implements an IoT structured wearable social sensing platform with the integration of privacy audio feature, behavior monitoring and environment sensing in a naturalistic environment. Particularly, four privacy protected audio-wellbeing features are embedded into the platform to automatically evaluate speech information without preserving raw audio data. Four weeks of long-term monitoring experimental studies have been conducted. A series of well-being questionnaires in conjunction with a group of students are engaged to objectively investigate the relationships between physical and mental health by utilizing the feature fusion strategy from speech, behavioral activities and ambient factors.

Publication metadata

Author(s): Yang S, Gao B, Jiang L, Jin J, Gao Z, Ma X, Woo WL

Publication type: Article

Publication status: Published

Journal: IEEE Internet of Things Journal

Year: 2019

Volume: 6

Issue: 2

Pages: 3652-3662

Print publication date: 01/04/2019

Online publication date: 27/12/2018

Acceptance date: 02/04/2018

ISSN (electronic): 2327-4662

Publisher: IEEE


DOI: 10.1109/JIOT.2018.2889966


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