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Proceedings Paper

Robustness of remote stress detection from visible spectrum recordings
Author(s): Balvinder Kaur; Sophia Moses; Megha Luthra; Vasiliki N. Ikonomidou; Elizabeth Tarbox
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Paper 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.

Paper Details

Date Published: 19 May 2016
PDF: 9 pages
Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 987104 (19 May 2016); doi: 10.1117/12.2223948
Show Author Affiliations
Balvinder Kaur, U.S. Army Research, Development and Engineering Command (United States)
George Mason Univ. (United States)
Sophia Moses, George Mason Univ. (United States)
Megha Luthra, George Mason Univ. (United States)
Vasiliki N. Ikonomidou, George Mason Univ. (United States)
Elizabeth Tarbox, George Mason Univ. (United States)


Published in SPIE Proceedings Vol. 9871:
Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016
Liyi Dai; Yufeng Zheng; Henry Chu; Anke D. Meyer-Bäse, Editor(s)

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