IoT Structured Long-Term Wearable Social Sensing for Mental Wellbeing

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), nonintrusive 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. 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.