  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.
