  Background: A smartphone is a promising tool for daily cardiovascular
measurement and mental stress monitoring. A smartphone camera-based
PhotoPlethysmoGraphy (PPG) and a low-cost thermal camera can be used to create
cheap, convenient and mobile monitoring systems. However, to ensure reliable
monitoring results, a person has to remain still for several minutes while a
measurement is being taken. This is very cumbersome and makes its use in
real-life mobile situations quite impractical.
  Objective: We propose a system which combines PPG and thermography with the
aim of improving cardiovascular signal quality and capturing stress responses
quickly.
  Methods: Using a smartphone camera with a low cost thermal camera added on,
we built a novel system which continuously and reliably measures two different
types of cardiovascular events: i) blood volume pulse and ii)
vasoconstriction/dilation-induced temperature changes of the nose tip. 17
healthy participants, involved in a series of stress-inducing mental workload
tasks, measured their physiological responses to stressors over a short window
of time (20 seconds) immediately after each task. Participants reported their
level of perceived mental stress using a 10-cm Visual Analogue Scale (VAS). We
used normalized K-means clustering to reduce interpersonal differences in the
self-reported ratings. For the instant stress inference task, we built novel
low-level feature sets representing variability of cardiovascular patterns. We
then used the automatic feature learning capability of artificial Neural
Networks (NN) to improve the mapping between the extracted set of features and
the self-reported ratings. We compared our proposed method with existing
hand-engineered features-based machine learning methods.
  Results, Conclusions: ... due to limited space here, we refer to our
manuscript.
