DistancePPG: Robust non-contact vital signs monitoring using a camera

By Mayank Kumar, Ashok Narayanan Veeraraghavan, Ashutosh Sabharwal. Biomedical Optics Express, 2015

411 citations recorded by the source corpus.

Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Surgery

View Paper: https://www.semanticscholar.org/paper/5e48c065703d1040f3b82526522066baf40eab0f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Xuesong Niu
2. Shiguang Shan
3. Xilin Chen
4. Hu Han
5. Sander Stuijk
6. Wenjin Wang
7. A. D. den Brinker
8. G. de Haan

In Cardiology and Cardiovascular Medicine

1. Sander Stuijk
2. Wenjin Wang
3. Xuesong Niu
4. Hu Han
5. Shiguang Shan
6. Xilin Chen
7. A. D. den Brinker
8. G. de Haan

Citations in the corpus

• Wireless Sensing for Human Activity: A Survey (2020, neutral): “, DistancePPG [129]) and sensor-based (e.”
• Algorithmic Principles of Remote PPG (2017, methodology): “For a thorough evaluation, we benchmark it with seven commonly used or state-of-the-art rPPG methods: 1) G (2007) [1], the single wavelength method that is still popular as evidenced by recent researches [7], [10].”
• RhythmNet: End-to-End Heart Rate Estimation From Face via Spatial-Temporal Representation (2019, methodology): “[21] Webcam G Patch level signals combination FFT using weighted average”
• Designing a Moral Compass for the Future of Computer Vision Using Speculative Analysis (2017, neutral): “Without a doubt, combining advanced machine learning with troves of image data is likely to aid human causes such as health monitoring [37] and accelerate efficiency in areas such as archiving [17] and traffic analysis [66].”