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In this paper, we define the term 'DigitalExposome' as a conceptual frameworkthat takes us closer towards understanding the relationship betweenenvironment, personal characteristics, behaviour and wellbeing using multimodelmobile sensing technology. Specifically, we simultaneously collected (for thefirst time) multi-sensor data including urban environmental factors (e.g. airpollution including: PM1, PM2.5, PM10, Oxidised, Reduced, NH3 and Noise, PeopleCount in the vicinity), body reaction (physiological reactions including: EDA,HR, HRV, Body Temperature, BVP and movement) and individuals' perceivedresponses (e.g. self-reported valence) in urban settings. Our users followed apre-specified urban path and collected the data using a comprehensive sensingedge devices. The data is instantly fused, time-stamped and geo-tagged at thepoint of collection. A range of multivariate statistical analysis techniqueshave been applied including Principle Component Analysis, Regression andspatial visualisations to unravel the relationship between the variables.Results showed that EDA and Heart Rate Variability HRV are noticeably impactedby the level of Particulate Matters (PM) in the environment well with theenvironmental variables. Furthermore, we adopted Deep Belief Network to extractfeatures from the multimodel data feed which outperformed Convolutional NeuralNetwork and achieved up to (a=80.8%, {\sigma}=0.001) accuracy.

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