StressNet: Detecting Stress in Thermal Videos

Satish Kumar, A S M Iftekhar, Michael Goebel, Thomas Bullock, Michael B. Miller, Tyler Santander, Barry Giesbrecht, Scott T. Grafton, B.S. Manjunath

Abstract:

Precise measurement of physiological signals is critical for the effective monitoring of human vital signs. Recent developments in computer vision have demonstrated
that signals such as pulse rate and respiration rate can
be extracted from digital video of humans, increasing the
possibility of contact-less monitoring. This paper presents
a novel approach to obtaining physiological signals and
classifying stress states from thermal video. The proposed
network–”StressNet”–features a hybrid emission representation model that models the direct emission and absorption of heat by the skin and underlying blood vessels. This
results in an information-rich feature representation of the
face, which is used by spatio-temporal network for reconstructing the ISTI ( Initial Systolic Time Interval : a measure
of change in cardiac sympathetic activity that is considered
to be a quantitative index of stress in humans). The reconstructed ISTI signal is fed into a stress-detection model to
detect and classify the individual’s stress state (i.e. stress or
no stress). A detailed evaluation demonstrates that StressNet achieves estimated the ISTI signal with 95% accuracy
and detect stress with average precision of 0.842