Robustness of remote stress detection from visible spectrum recordings

Balvinder Kaur; Sophia Moses; Megha Luthra; Vasiliki N. Ikonomidou; Elizabeth Tarbox

doi:10.1117/12.2223948

19 May 2016 Robustness of remote stress detection from visible spectrum recordings

Balvinder Kaur, Sophia Moses, Megha Luthra, Vasiliki N. Ikonomidou, Elizabeth Tarbox

Proceedings Volume 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016; 987104 (2016) https://doi.org/10.1117/12.2223948
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

Abstract

In our recent work, we have shown that it is possible to extract high fidelity timing information of the cardiac pulse wave from visible spectrum videos, which can then be used as a basis for stress detection. In that approach, we used both heart rate variability (HRV) metrics and the differential pulse transit time (dPTT) as indicators of the presence of stress. One of the main concerns in this analysis is its robustness in the presence of noise, as the remotely acquired signal that we call blood wave (BW) signal is degraded with respect to the signal acquired using contact sensors. In this work, we discuss the robustness of our metrics in the presence of multiplicative noise. Specifically, we study the effects of subtle motion due to respiration and changes in illumination levels due to light flickering on the BW signal, the HRV-driven features, and the dPTT. Our sensitivity study involved both Monte Carlo simulations and experimental data from human facial videos, and indicates that our metrics are robust even under moderate amounts of noise. Generated results will help the remote stress detection community with developing requirements for visual spectrum based stress detection systems.

Citation Download Citation

Balvinder Kaur, Sophia Moses, Megha Luthra, Vasiliki N. Ikonomidou, and Elizabeth Tarbox "Robustness of remote stress detection from visible spectrum recordings", Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 987104 (19 May 2016); https://doi.org/10.1117/12.2223948

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available