StressNet: Detecting Stress in Thermal Videos

TL;DR

StressNet is a novel thermal video-based method that accurately reconstructs cardiac signals and classifies stress states, enabling contactless physiological monitoring with high precision.

Contribution

The paper introduces StressNet, a hybrid emission model and spatio-temporal network for extracting ISTI signals and detecting stress from thermal videos, advancing contactless monitoring techniques.

Findings

ISTI signal reconstructed with 95% accuracy

Stress detection achieved an average precision of 0.842

Method enables contactless, non-invasive stress monitoring

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. The source code is available on Github.