Feasibility of Contactless Pulse Rate Monitoring of Neonates using Google Glass

Fernando, Shakith; Wang, Wenjin; Kirenko, Ihor; Haan, G. de; Oetomo, Sidarto Bambang; Corporaal, Henk; Dalfsen, Jan van

doi:10.4108/eai.14-10-2015.2261589

Cited by 19 publications

(15 citation statements)

References 13 publications

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“…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%

Algorithmic Principles of Remote PPG

Wang

Brinker

Stuijk

et al. 2017

IEEE Trans. Biomed. Eng.

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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: Introductionmentioning

confidence: 99%

Algorithmic Principles of Remote PPG

Wang

Brinker

Stuijk

et al. 2017

IEEE Trans. Biomed. Eng.

Self Cite

716

633

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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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“…Video-based physiological measurement has advantages over contact sensors, perhaps the most significant being that ubiquitous low-cost cameras can be used, negating the need for customized hardware [1], [2]. In addition, the vital signs of multiple people can be measured concomitantly using the same sensor [3] and head-worn cameras can capture this signal even in the presence of motion [4], [5], [6]. Videobased methods can be used to measure the blood volume pulse [3], heart rate (HR), respiration, heart rate variability (HRV) and blood oxygenation [7].…”

Section: Introductionmentioning

confidence: 99%

InPhysible: Camouflage Against Video-Based Physiological Measurement

McDuff

Hurter²

2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Imaging photoplethysmography (iPPG) is a powerful set of methods for measuring physiological signals from video. Recent advances have shown that a low-cost webcam can be used to measure heart rate, blood flow, respiration, blood oxygen levels and stress. While these methods have many beneficial applications, the unobtrusive and ubiquitous nature of the sensors risk exposing people to unwanted measurement. We present InPhysible the first camouflage system against videobased physiological measurement. The infra-red system can be embedded into any pair of glasses, or other headwear, and disrupts the measurement of the iPPG signal while being imperceptible by the human eye. Our system is flexible and can simulate realistic pulse signals to hinder heart rate measurement. In this paper we present the design of our prototype and a user study validating its efficacy. Finally, we discuss the limitations and implications for data privacy and security.

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Feasibility of Contactless Pulse Rate Monitoring of Neonates using Google Glass

Cited by 19 publications

References 13 publications

Algorithmic Principles of Remote PPG

Algorithmic Principles of Remote PPG

Robust heart rate from fitness videos

InPhysible: Camouflage Against Video-Based Physiological Measurement

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