  The quantification of emotional states is an important step to understanding
wellbeing. Time series data from multiple modalities such as physiological and
motion sensor data have proven to be integral for measuring and quantifying
emotions. Monitoring emotional trajectories over long periods of time inherits
some critical limitations in relation to the size of the training data. This
shortcoming may hinder the development of reliable and accurate machine
learning models. 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.
Furthermore, the possibility of performing TL to infer stress from
physiological data is explored by initially training a 1D CNN using a large
physical activity dataset and then applying the learned knowledge to the target
dataset. We demonstrate that model performance when inferring real-world
wellbeing rated on a 5-point Likert scale can be enhanced using our framework,
resulting in up to 98.5% accuracy, outperforming a conventional CNN by 4.5%.
Subject-independent models using the same approach resulted in an average of
72.3% accuracy (SD 0.038). The proposed CNN-TL-based methodology may overcome
problems with small training datasets, thus improving on the performance of
conventional deep learning methods.
