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DigitalExposome: Quantifying the Urban Environment Influence on Wellbeing based on Real-Time Multi-Sensor Fusion and Deep Belief Network
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.
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification
To address this problem, this paper proposes a framework to tackle the limitation in performing emotional state recognition on multiple multimodal datasets: 1) encoding multivariate time series data into coloured images; 2) leveraging pre-trained object recognition models to apply a Transfer Learning (TL) approach using the images from step 1; 3) utilising a 1D Convolutional Neural Network (CNN) to perform emotion classification from physiological data; 4) concatenating the pre-trained TL model with the 1D CNN.
On-Device Transfer Learning for Personalising Psychological Stress Modelling using a Convolutional Neural Network
Stress is a growing concern in modern society adversely impacting the wider population more than ever before.
LabelSens: Enabling Real-time Sensor Data Labelling at the point of Collection on Edge Computing
Labelling is an indispensable stage of data pre-processing that can be particularly challenging, especially when applied to single or multi-model real-time sensor data collection approaches.
