DigitalExposome: Quantifying the Urban Environment Influence on Wellbeing based on Real-Time Multi-Sensor Fusion and Deep Belief Network

TL;DR

This study introduces the 'DigitalExposome' framework, combining multi-sensor data and deep learning to analyze how urban environmental factors influence individual wellbeing in real-time.

Contribution

It is the first to simultaneously collect and fuse multi-sensor data on environment, physiology, and perception, applying deep learning for improved analysis of wellbeing impacts.

Findings

EDA and HRV are significantly affected by particulate matter levels.

Deep Belief Network outperforms CNN with up to 80.8% accuracy.

Multivariate analysis reveals relationships between environment and physiological responses.

Abstract

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 multimodel mobile sensing technology. Specifically, we simultaneously collected (for the first time) multi-sensor data including urban environmental factors (e.g. air pollution including: PM1, PM2.5, PM10, Oxidised, Reduced, 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 devices. 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 EDA and Heart Rate Variability HRV are noticeably impacted by the level of Particulate Matters (PM) in the environment well with the environmental variables. Furthermore, we adopted Deep Belief Network to extract features from the multimodel data feed which outperformed Convolutional Neural Network and achieved up to (a=80.8%, {\sigma}=0.001) accuracy.