IOT structured Long-term Wearable Social Sensing for Mental Wellbeing

Yang, Sihao, Gao, Bin, Jiang, Long, Jin, Jikun, Gao, Zhao, Ma, Xiaole and Woo, Wai Lok (2018) IOT structured Long-term Wearable Social Sensing for Mental Wellbeing. IEEE Internet of Things Journal. ISSN 2372-2541

Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/JIOT.2018.2889966

Abstract

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.

Item Type: Article
Uncontrolled Keywords: Internet of things, Long-term monitoring, Mental health, Wearable device, feature fusion and classification
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 27 Mar 2019 11:31
Last Modified: 27 Mar 2019 11:31
URI: http://nrl.northumbria.ac.uk/id/eprint/38561

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