Browse by author
Lookup NU author(s): Professor Bin Gao, Dr Long Jiang, Dr Wai Lok Woo
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
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.
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
URL: https://doi.org/10.1109/JIOT.2018.2889966
DOI: 10.1109/JIOT.2018.2889966
Altmetrics provided by Altmetric
