Wearable Social Sensing: Content-Based Processing Methodology and Implementation

Gu, Jun, Gao, Bin, Chen, Yuanpeng, Jiang, Long, Gao, Zhao, Ma, Xiaole, Ma, Yong, Woo, Wai Lok and Jin, Jikun (2017) Wearable Social Sensing: Content-Based Processing Methodology and Implementation. IEEE Sensors Journal, 17 (21). pp. 7167-7176. ISSN 1530-437X

Full text not available from this repository.

Abstract

Developing wearable activity and speech sensing for assessing human physical and mental health is just as significant as conscious content for determining social behavior. Multiple social relevant sensors, such as microphones and accelerometer, embedded in wearable devices paves the way to provide the opportunity to continuously and non-invasively monitor anxiety and stress in real-life situation. In this paper, we present the design, implementation, and deployment of a wearable computing platform capable of automatically extracting and analyzing social signals. In particular, we benchmarked a set of integrated social features to objectively quantify the level of anxiety using an in-house built wearable device. In addition, in order to protect privacy, we propose a potential method to embed the audio features processing in the hardware to avoid recording their voice directly. In addition, we have implemented the k-means classification to determine the level of anxiety of the subjects. The obtained performance has demonstrated that both activity and speech social features have the potential to directly infer anxiety across multiple individuals.

Item Type:	Article
Uncontrolled Keywords:	Social signal processing, audio and activity features, wearable device
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	25 Mar 2019 10:22
Last Modified:	10 Oct 2019 21:03
URI:	http://nrl.northumbria.ac.uk/id/eprint/38521

Downloads