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Signal Processing of Multimodal Mobile Lifelogging Data towards Detecting Stress in Real-World Driving

Dobbins, CM and Fairclough, SH (2018) Signal Processing of Multimodal Mobile Lifelogging Data towards Detecting Stress in Real-World Driving. IEEE Transactions on Mobile Computing. ISSN 1536-1233

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Abstract

Stress is a negative emotion that is part of everyday life. However, frequent episodes or prolonged periods of stress can be detrimental to long-term health. Nevertheless, developing self-awareness is an important aspect of fostering effective ways to self-regulate these experiences. Mobile lifelogging systems provide an ideal platform to support self-regulation of stress by raising awareness of negative emotional states via continuous recording of psychophysiological and behavioural data. However, obtaining meaningful information from large volumes of raw data represents a significant challenge because these data must be accurately quantified and processed before stress can be detected. This work describes a set of algorithms designed to process multiple streams of lifelogging data for stress detection in the context of real world driving. Two data collection exercises have been performed where multimodal data, including raw cardiovascular activity and driving information, were collected from twenty-one people during daily commuter journeys. Our approach enabled us to 1) pre-process raw physiological data to calculate valid measures of heart rate variability, a significant marker of stress, 2) identify/correct artefacts in the raw physiological data and 3) provide a comparison between several classifiers for detecting stress. Results were positive and ensemble classification models provided a maximum accuracy of 86.9% for binary detection of stress in the real-world.

Item Type: Article
Additional Information: Dobbins, C.M. & Fairclough, S.H. Signal Processing of Multimodal Mobile Lifelogging Data towards Detecting Stress in Real-World Driving. IEEE Transactions on Mobile Computing. 24/05/18. http://dx.doi.org/10.1109/TMC.2018.2840153
Uncontrolled Keywords: 0805 Distributed Computing, 1005 Communications Technologies, 0906 Electrical And Electronic Engineering
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science
Natural Sciences and Psychology
Publisher: Institute of Electrical and Electronics Engineers
Date Deposited: 22 May 2018 09:52
Last Modified: 04 Oct 2018 19:35
DOI or Identification number: 10.1109/TMC.2018.2840153
URI: http://researchonline.ljmu.ac.uk/id/eprint/8692

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