Remote stress detection using a visible spectrum camera

Kaur, Bhavneet; Moses, Sophia; Luthra, Megha; Ikonomidou, Vasiliki N.

doi:10.1117/12.2177159

Cited by 5 publications

(10 citation statements)

References 33 publications

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“…Overall, our results demonstrate that the BDC based detected peaks are highly correlated with the GT, and therefore, can help with generating cleaner RR-interval signals, which is the first step for HRV analysis [6]. While further research is needed to improve both acquisition and data processing strategies, we believe that our study shows the potential of a non-contact, possibly covert system for remote vital sign detection, which can be used for many military, medical and security applications.…”

Section: Discussion and Summarymentioning

confidence: 80%

“…We determined the BW signals by applying our method for remote cardiac signal detection [6] to the experimental data. These BW signals had multiple peaks (similar to the BW signal shown in Figure 2).…”

Section: Peak Detection From Signals With Single Peaksmentioning

confidence: 99%

“…This makes the extraction of the RR intervals, which is the first step for heart rate variability (HRV) analysis, a very challenging problem. Time (sec) 8 9 10 In our previous work, we presented a pipeline for RR-interval signal generation, including BW signal generation and period detection based on zero crossings between subsequent cycles [6]. BW is the remotely measured heartbeat signal.…”

Section: Introductionmentioning

confidence: 99%

“…Although a variety of algorithms have been developed to determine clean blood wave (BW) signals [1]- [6], extracting the exact timing information, a process that for the ECG signal involves calculating the positions of the R-peaks of the cardiac waveform, poses several difficulties for the BW signal as it requires estimating the equivalent peak positions in the BW signal. There are two main factors that would complicate it: pulse broadening, due to both the low-pass nature of the cardiovascular system and multipath propagation effects [7], [8]; and the presence of multiple peaks, due to both reflected waves and additive noise [9], [10] .…”

Section: Introductionmentioning

confidence: 99%

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Remote heartbeat signal detection from visible spectrum recordings based on blind deconvolution

et al. 2016

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While recent advances have shown that it is possible to acquire a signal equivalent to the heartbeat from visual spectrum video recordings of the human skin, extracting the heartbeat's exact timing information from it, for the purpose of heart rate variability analysis, remains a challenge. In this paper, we explore two novel methods to estimate the remote cardiac signal peak positions, aiming at a close representation of the R-peaks of the ECG signal. The first method is based on curve fitting (CF) using a modified filtered least mean square (LMS) optimization and the second method is based on system estimation using blind deconvolution (BDC). To prove the efficacy of the developed algorithms, we compared results obtained with the ground truth (ECG) signal. Both methods achieved a low relative error between the peaks of the two signals. This work, performed under an IRB approved protocol, provides initial proof that blind deconvolution techniques can be used to estimate timing information of the cardiac signal closely correlated to the one obtained by traditional ECG. The results show promise for further development of a remote sensing of cardiac signals for the purpose of remote vital sign and stress detection for medical, security, military and civilian applications.

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Section: Discussion and Summarymentioning

confidence: 80%

Section: Peak Detection From Signals With Single Peaksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Remote heartbeat signal detection from visible spectrum recordings based on blind deconvolution

et al. 2016

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“…However, recent studies have shown that a heartbeat signal can be measured remotely from visible spectrum recordings based on minor intensity fluctuations [1]- [5]. In our own studies, we have shown that the remotely measured heartbeat signals from two distal body locations can also be used to determine differential pulse transit time (dPTT), which is the difference in the time of arrival of the blood wave (BW) between two remote areas of the body [6], an indirect indicator of pulse transit time (PTT).…”

Section: Introductionmentioning

confidence: 99%

Robustness of remote stress detection from visible spectrum recordings

et al. 2016

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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.

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