Remote measurement of cognitive stress via heart rate variability

McDuff, Daniel; Gontarek, Sarah; Picard, Rosalind W.

doi:10.1109/embc.2014.6944243

Cited by 247 publications

(147 citation statements)

References 18 publications

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“…SinceΣ is estimated from the video content, the key-point of PBV is in defining the blood volume pulse vector u pbv 3 . PBV has a clear advantage: when the assumption of (17) holds, the estimated projection-axis is optimal for pulse retrieval. However, it has two limitations.…”

Section: B Model-based Methods (Pbv/chrom)mentioning

confidence: 99%

“…The essential difference between these rPPG methods is in the way of combining RGB-signals into a pulse-signal. A better understanding of the core rPPG methods could benefit many systems/applications for video health monitoring, such as the monitoring of heart-rate [7]- [11], respiration [8], SpO 2 [8], [12], blood pressure [13], neonates [14], [15], and the detection of atrial fibrillation [16] and mental stress [17].…”

Section: Introductionmentioning

confidence: 99%

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Algorithmic Principles of Remote PPG

Wang

Brinker

Stuijk

et al. 2017

IEEE Trans. Biomed. Eng.

828

633

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Wang, W.; den Brinker, A.C.; Stuijk, S.; de Haan, G. Published in: IEEE Transactions on Biomedical Engineering DOI:10.1109/TBME.2016.2609282Published: 01/07/2017 Document VersionAuthor's version before peer-review Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication Citation for published version (APA):Wang, W., den Brinker, A. C., Stuijk, S., & de Haan, G. (2017). Algorithmic principles of remote-PPG. IEEE Transactions on Biomedical Engineering, 64(7), 1479-1491. DOI: 10.1109/TBME.2016.2609282 General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract-This paper introduces a mathematical model that incorporates the pertinent optical and physiological properties of skin reflections with the objective to increase our understanding of the algorithmic principles behind remote photoplethysmography (rPPG). The model is used to explain the different choices that were made in existing rPPG methods for pulse extraction. The understanding that comes from the model can be used to design robust or application-specific rPPG solutions. We illustrate this by designing an alternative rPPG method where a projection plane orthogonal to the skin-tone is used for pulse extraction. A large benchmark on the various discussed rPPG methods shows that their relative merits can indeed be understood from the proposed model.

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Section: B Model-based Methods (Pbv/chrom)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Algorithmic Principles of Remote PPG

Wang

Brinker

Stuijk

et al. 2017

IEEE Trans. Biomed. Eng.

828

633

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show abstract

“…Common physiological measurements to evaluate workload are heart rate variability (HRV) (McDuff et al, 2014), electroencephalography (EEG) (Wilson and Russell, 2003), pupillary response (Iqbal and Bailey, 2005), and galvanic skin response (GSR) (Nourbakhsh et al, 2012).…”

Section: Physiological Measurements Of Workloadmentioning

confidence: 99%

Steering a Ship - Investigating Affective State and Workload in Ship Simulations

Dybvik¹,

Wulvik²,

Steinert³

2018

Proceedings of the DESIGN 2018 15th International Design Conference

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We present an experiment investigating concepts of affective state and workload in a large ship manoeuvring context. It is run on a consumer ship simulator software where student participants (N=31) perform two ecologically valid scenarios: sailing on open sea and in a harbour. Results from surveys show highly significant changes in terms of both affect and workload between the scenarios. Thus, one should consider varying affects and workloads from users in varying contexts, consequently demanding new design paradigms for product development, such as dynamically adaptive interfaces.

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“…The fundamental use of rPPG leads to various applications for video health monitoring, enabling non-contact measurement of physiological parameters from a human body, such as heart-rate (Li et al 2014, Tarassenko et al 2014, Kumar et al 2015, Wang et al 2015a, Tulyakov et al 2016, heart-rate variability (Blackford et al 2016), respiration (Tarassenko et al 2014), SpO 2 (Guazzi et al 2015), pulse transit time (Shao et al 2014), blood pressure (Jeong et al 2016), atrial fibrillation (Couderc et al 2015), mental stress (McDuff et al 2014a), monitoring of neonates (Mestha et al 2014, Fernando et al 2015, living-skin detection for face anti-spoofing (Gibert et al 2013, Wang et al 2015b, Liu et al 2016, etc. In addition to the clinical and home-based applications, the rPPG technique would also be attractive in the gym.…”

Section: Introductionmentioning

confidence: 99%

Robust heart rate from fitness videos

Wang

Brinker²,

Stuijk

et al. 2017

Physiol. Meas.

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Remote photoplethysmography (rPPG) enables contactless heart-rate monitoring using a regular video camera. Objective: This paper aims to improve the rPPG technology targeting continuous heart-rate measurement during fitness exercises. The fundamental limitation of the existing (multi-wavelength) rPPG methods is that they can suppress at most n − 1 independent distortions by linearly combining n wavelength color channels. Their performance are highly restricted when more than n − 1 independent distortions appear in a measurement, as typically occurs in fitness applications with vigorous body motions. Approach: To mitigate this limitation, we propose an effective yet very simple method that algorithmically extends the number of possibly suppressed distortions without using more wavelengths. Our core idea is to increase the degrees-of-freedom of noise reduction by decomposing the n wavelength camera-signals into multiple orthogonal frequency bands and extracting the pulse-signal per band-basis. This processing, namely Sub-band rPPG (SB), can suppress different distortion-frequencies using independent combinations of color channels. Main results: A challenging fitness benchmark dataset is created, including 25 videos recorded from 7 healthy adult subjects (ages from 25 to 40 yrs; six male and one female) running on a treadmill in an indoor environment. Various practical challenges are simulated in the recordings, such as different skin-tones, light sources, illumination intensities, and exercising modes. The basic form of SB is benchmarked against a state-of-the-art method (POS) on the fitness dataset. Using non-biased parameter settings, the average signal-tonoise-ratio (SNR) for POS varies in [−4.18, −2.07] dB, for SB varies in [−1.08, 4.77] dB. The ANOVA test shows that the improvement of SB over POS is statistically significant for almost all settings (p-value <0.05). Significance: The results suggest that the proposed SB method considerably increases the

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Remote measurement of cognitive stress via heart rate variability

Cited by 247 publications

References 18 publications

Algorithmic Principles of Remote PPG

Algorithmic Principles of Remote PPG

Steering a Ship - Investigating Affective State and Workload in Ship Simulations

Robust heart rate from fitness videos

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