DigitalExposome: quantifying impact of urban environment on wellbeing using sensor fusion and deep learning

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Johnson, T ORCID: https://orcid.org/0000-0002-1702-0943, Kanjo, E ORCID: https://orcid.org/0000-0002-1720-0661 and Woodward, K ORCID: https://orcid.org/0000-0003-3302-1345, 2023. DigitalExposome: quantifying impact of urban environment on wellbeing using sensor fusion and deep learning. Computational Urban Science, 3 (1): 14. ISSN 2730-6852

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Official URL: https://doi.org/10.1007/s43762-023-00088-9

Abstract

The increasing level of air pollutants (e.g. particulates, noise and gases) within the atmosphere are impacting mental wellbeing. In this paper, we define the term 'DigitalExposome' as a conceptual framework that takes us closer towards understanding the relationship between environment, personal characteristics, behaviour and wellbeing using multimodal mobile sensing technology. Specifically, we simultaneously collected (for the first time) multi-sensor data including urban environmental factors (e.g. air pollution including: Particulate Matter (PM1), (PM2.5), (PM10), Oxidised, Reduced, Ammonia (NH3) and Noise, People Count in the vicinity), body reaction (physiological reactions including: EDA, HR, HRV, Body Temperature, BVP and movement) and individuals' perceived responses (e.g. self-reported valence) in urban settings. Our users followed a pre-specified urban path and collected the data using a comprehensive sensing edge device. The data is instantly fused, time-stamped and geo-tagged at the point of collection. A range of multivariate statistical analysis techniques have been applied including Principle Component Analysis, Regression and Spatial Visualisations to unravel the relationship between the variables. Results showed that Electrodermal Activity (EDA) and Heart Rate Variability (HRV) are noticeably impacted by the level of Particulate Matter in the environment. Furthermore, we adopted Convolutional Neural Network (CNN) to classify self-reported wellbeing from the multimodal dataset which achieved an f1-score of 0.76.

Item Type:	Journal article
Publication Title:	Computational Urban Science
Creators:	Johnson, T., Kanjo, E. and Woodward, K.
Publisher:	Springer
Date:	2023
Volume:	3
Number:	1
ISSN:	2730-6852
Identifiers:	Number Type 10.1007/s43762-023-00088-9 DOI 1739767 Other
Rights:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	12 Jun 2023 12:32
Last Modified:	12 Jun 2023 12:32
URI:	https://irep.ntu.ac.uk/id/eprint/49172