Harris, B, Dobbins, C, Fairclough, SH and Lisboa, P (2017) Exploring Wearable Devices for Unobtrusive Stress Monitoring. In: ACM International Conference Proceedings Series . (Second International Conference on Internet of Things, Data and Cloud Computing, 22 – 23 March, 2017, Cambridge, UK).

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Abstract

Recent advances in modern technology have seen the proliferation of low-cost commercial sensors which are capable of unobtrusively obtaining diverse physiological datasets to identify the presence of psychological stress. In particular, sensor-orientated smartwatches have the potential to assist a person in multiple facets of their daily life. These devices may be used as a tool for collecting physiological and contextual datasets in the wild. This paper explores the current literature on application of machine-learning techniques in stress system studies. This informs the selection of appropriate methodologies for data intensive research to support accurate inferences of the presence of stress.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Stress; Classification; Physiological Computing; Sensor Technology; Wearable Devices
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science & Mathematics
Publisher:	ACM
Date Deposited:	02 Nov 2016 14:52
Last Modified:	15 Aug 2024 12:39
URI:	https://researchonline.ljmu.ac.uk/id/eprint/4743

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