  We propose DeepBreath, a deep learning model which automatically recognises
people's psychological stress level (mental overload) from their breathing
patterns. Using a low cost thermal camera, we track a person's breathing
patterns as temperature changes around his/her nostril. The paper's technical
contribution is threefold. First of all, instead of creating hand-crafted
features to capture aspects of the breathing patterns, we transform the
uni-dimensional breathing signals into two dimensional respiration variability
spectrogram (RVS) sequences. The spectrograms easily capture the complexity of
the breathing dynamics. Second, a spatial pattern analysis based on a deep
Convolutional Neural Network (CNN) is directly applied to the spectrogram
sequences without the need of hand-crafting features. Finally, a data
augmentation technique, inspired from solutions for over-fitting problems in
deep learning, is applied to allow the CNN to learn with a small-scale dataset
from short-term measurements (e.g., up to a few hours). The model is trained
and tested with data collected from people exposed to two types of cognitive
tasks (Stroop Colour Word Test, Mental Computation test) with sessions of
different difficulty levels. Using normalised self-report as ground truth, the
CNN reaches 84.59% accuracy in discriminating between two levels of stress and
56.52% in discriminating between three levels. In addition, the CNN
outperformed powerful shallow learning methods based on a single layer neural
network. Finally, the dataset of labelled thermal images will be open to the
community.
