# StressNet: Detecting Stress in Thermal Videos

arXiv: https://arxiv.org/abs/2011.09540 · HTML: https://mlsanity.com/paper/2011.09540/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [MethaneMapper: Spectral Absorption aware Hyperspectral Transformer for Methane Detection](https://mlsanity.com/paper/2304.02767/) (2023)
- [GTNet:Guided Transformer Network for Detecting Human-Object Interactions](https://mlsanity.com/paper/2108.00596/) (2021)
- [LOCL: Learning Object-Attribute Composition using Localization](https://mlsanity.com/paper/2210.03780/) (2022)
- ["Reading Between the Heat": Co-Teaching Body Thermal Signatures for Non-intrusive Stress Detection](https://mlsanity.com/paper/2310.09932/) (2023)
- [In-situ Water quality monitoring in Oil and Gas operations](https://mlsanity.com/paper/2301.08800/) (2023)
- [Improving Minority Stress Detection with Emotions](https://mlsanity.com/paper/2311.17676/) (2023)

## Abstract (arXiv)

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.