Impacts of Environmental Factors on Wellbeing: Machine Learning-Based Benchmarking of Spatial and Temporal Properties

Faiza Guerrache; David J. Brown; Mufti Mahmud

doi:10.1007/978-981-97-3302-6_51

Impacts of Environmental Factors on Wellbeing: Machine Learning-Based Benchmarking of Spatial and Temporal Properties

Faiza Guerrache
, David J. Brown
, Mufti Mahmud^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Environmental noise impacts wellbeing with consequences on physical and mental health. Chronic exposure to noise pollution can amplify stress levels, disturb sleep patterns, and increase the risk of cardiovascular issues. Individuals subjected to continuous noise often report high irritability, impaired concentration, and diminished cognitive performance which may lead to anxiety and depression, particularly in vulnerable populations, such as children and the elderly. Recognising the profound impact of environmental noise is crucial for public health, necessitating comprehensive strategies to mitigate and regulate noise pollution for the enhancement of overall wellbeing. Despite the recent advancement of artificial intelligence (AI), this issue is under-addressed as no benchmark dataset is available to perform an in-depth analysis of the factors that subtly and gradually contribute to the deterioration of physical and mental wellbeing. To fill this gap, the current work presents a benchmarking dataset and explores the spatial and temporal properties of the same in understanding how these environmental factors affect wellbeing. Experiments with explainable AI (XAI) indicate that the Random Forest and Gradient Boosting classifiers can be considered as baseline XAI methods to analyse the outdoor and indoor environmental data, respectively.

Original language	English
Title of host publication	Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024
Editors	Xin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	631-642
Number of pages	12
ISBN (Print)	9789819733019
DOIs	https://doi.org/10.1007/978-981-97-3302-6_51
State	Published - 2024
Externally published	Yes
Event	9th International Congress on Information and Communication Technology, ICICT 2024 - London, United Kingdom Duration: 19 Feb 2024 → 22 Feb 2024

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1003 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	9th International Congress on Information and Communication Technology, ICICT 2024
Country/Territory	United Kingdom
City	London
Period	19/02/24 → 22/02/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Body temperature
ECG
Environment stress prediction
Environmental health
Hr
Machine learning
Multi sensor fusion
Multimodal
Physiological response
Urban noise
Wellbeing

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-97-3302-6_51

Cite this

Guerrache, F., Brown, D. J., & Mahmud, M. (2024). Impacts of Environmental Factors on Wellbeing: Machine Learning-Based Benchmarking of Spatial and Temporal Properties. In X.-S. Yang, S. Sherratt, N. Dey, & A. Joshi (Eds.), Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024 (pp. 631-642). (Lecture Notes in Networks and Systems; Vol. 1003 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-3302-6_51

Guerrache, Faiza ; Brown, David J. ; Mahmud, Mufti. / Impacts of Environmental Factors on Wellbeing : Machine Learning-Based Benchmarking of Spatial and Temporal Properties. Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. editor / Xin-She Yang ; Simon Sherratt ; Nilanjan Dey ; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2024. pp. 631-642 (Lecture Notes in Networks and Systems).

@inproceedings{d76560b865304a42b51a95066a6ec947,

title = "Impacts of Environmental Factors on Wellbeing: Machine Learning-Based Benchmarking of Spatial and Temporal Properties",

abstract = "Environmental noise impacts wellbeing with consequences on physical and mental health. Chronic exposure to noise pollution can amplify stress levels, disturb sleep patterns, and increase the risk of cardiovascular issues. Individuals subjected to continuous noise often report high irritability, impaired concentration, and diminished cognitive performance which may lead to anxiety and depression, particularly in vulnerable populations, such as children and the elderly. Recognising the profound impact of environmental noise is crucial for public health, necessitating comprehensive strategies to mitigate and regulate noise pollution for the enhancement of overall wellbeing. Despite the recent advancement of artificial intelligence (AI), this issue is under-addressed as no benchmark dataset is available to perform an in-depth analysis of the factors that subtly and gradually contribute to the deterioration of physical and mental wellbeing. To fill this gap, the current work presents a benchmarking dataset and explores the spatial and temporal properties of the same in understanding how these environmental factors affect wellbeing. Experiments with explainable AI (XAI) indicate that the Random Forest and Gradient Boosting classifiers can be considered as baseline XAI methods to analyse the outdoor and indoor environmental data, respectively.",

keywords = "Body temperature, ECG, Environment stress prediction, Environmental health, Hr, Machine learning, Multi sensor fusion, Multimodal, Physiological response, Urban noise, Wellbeing",

author = "Faiza Guerrache and Brown, \{David J.\} and Mufti Mahmud",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 9th International Congress on Information and Communication Technology, ICICT 2024 ; Conference date: 19-02-2024 Through 22-02-2024",

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Guerrache, F, Brown, DJ & Mahmud, M 2024, Impacts of Environmental Factors on Wellbeing: Machine Learning-Based Benchmarking of Spatial and Temporal Properties. in X-S Yang, S Sherratt, N Dey & A Joshi (eds), Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. Lecture Notes in Networks and Systems, vol. 1003 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 631-642, 9th International Congress on Information and Communication Technology, ICICT 2024, London, United Kingdom, 19/02/24. https://doi.org/10.1007/978-981-97-3302-6_51

Impacts of Environmental Factors on Wellbeing: Machine Learning-Based Benchmarking of Spatial and Temporal Properties. / Guerrache, Faiza; Brown, David J.; Mahmud, Mufti.
Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. ed. / Xin-She Yang; Simon Sherratt; Nilanjan Dey; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2024. p. 631-642 (Lecture Notes in Networks and Systems; Vol. 1003 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Guerrache F, Brown DJ, Mahmud M. Impacts of Environmental Factors on Wellbeing: Machine Learning-Based Benchmarking of Spatial and Temporal Properties. In Yang XS, Sherratt S, Dey N, Joshi A, editors, Proceedings of 9th International Congress on Information and Communication Technology - ICICT 2024. Springer Science and Business Media Deutschland GmbH. 2024. p. 631-642. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-97-3302-6_51