Real-Time Physiological Measurement and Visualization Using a Synchronized Multi-camera System

Remote physiological measurement has widespread implications in healthcare and affective computing. This paper presents an efficient system for remotely measuring heart rate and heart rate variability using multiple low-cost digital cameras in real-time. We combine an RGB camera, monochrome camera with color filter and a thermal camera to recover the blood volume pulse (BVP). We show that using multiple cameras in synchrony yields the most accurate recovery of the BVP signal. The RGB combination is not optimal. We show that the thermal camera improves performance of measurement under dynamic ambient lighting but the thermal camera alone is not enough and accuracy can be improved by adding more spectral channels. We present a real-time prototype that allows accurate physiological measurement combined with a novel user interface to visualize changes in heart rate and heart rate variability. Finally, we propose how this system might be used for applications such as patient monitoring.

[1]  Frédéric Bousefsaf,et al.  Remote detection of mental workload changes using cardiac parameters assessed with a low-cost webcam , 2014, Comput. Biol. Medicine.

[2]  Yu Sun,et al.  Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise. , 2011, Journal of biomedical optics.

[3]  Daniel McDuff,et al.  Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam , 2011, IEEE Transactions on Biomedical Engineering.

[4]  L. O. Svaasand,et al.  Remote plethysmographic imaging using ambient light. , 2008, Optics express.

[5]  Steffen Leonhardt,et al.  Hybrid optical imaging technology for long-term remote monitoring of skin perfusion and temperature behavior , 2014, Journal of biomedical optics.

[6]  Aapo Hyvrinen,et al.  Fast and Robust Fixed-Point Algorithms , 1999 .

[7]  L. Tarassenko,et al.  Non-contact video-based vital sign monitoring using ambient light and auto-regressive models , 2014, Physiological measurement.

[8]  Mika P. Tarvainen,et al.  An advanced detrending method with application to HRV analysis , 2002, IEEE Transactions on Biomedical Engineering.

[9]  Marc Garbey,et al.  Contact-Free Measurement of Cardiac Pulse Based on the Analysis of Thermal Imagery , 2007, IEEE Transactions on Biomedical Engineering.

[10]  Daniel McDuff,et al.  A survey of remote optical photoplethysmographic imaging methods , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[11]  Daniel McDuff,et al.  COGCAM: Contact-free Measurement of Cognitive Stress During Computer Tasks with a Digital Camera , 2016, CHI.

[12]  Westgate Road,et al.  Photoplethysmography and its application in clinical physiological measurement , 2007 .

[13]  Daniel McDuff,et al.  Improvements in Remote Cardiopulmonary Measurement Using a Five Band Digital Camera , 2014, IEEE Transactions on Biomedical Engineering.

[14]  Mayank Kumar,et al.  Robust acquisition of Photoplethysmograms using a Camera , 2014 .

[15]  Jenshan Lin,et al.  A microwave radio for Doppler radar sensing of vital signs , 2001, 2001 IEEE MTT-S International Microwave Sympsoium Digest (Cat. No.01CH37157).

[16]  Ethan B. Blackford,et al.  Recovering pulse rate during motion artifact with a multi-imager array for non-contact imaging photoplethysmography , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[17]  Rosalind W. Picard,et al.  Non-contact, automated cardiac pulse measurements using video imaging and blind source separation , 2022 .

[18]  Frédo Durand,et al.  Detecting Pulse from Head Motions in Video , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  R. Cohen,et al.  Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. , 1981, Science.

[20]  Luc Van Gool,et al.  Real-time facial feature detection using conditional regression forests , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Desok Kim,et al.  Detection of subjects with higher self-reporting stress scores using heart rate variability patterns during the day , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[22]  Frédo Durand,et al.  Eulerian video magnification for revealing subtle changes in the world , 2012, ACM Trans. Graph..

[23]  Valery V. Tuchin,et al.  Pulse-wave monitoring by means of focused laser beams scattered by skin surface and membranes , 1993, Photonics West - Lasers and Applications in Science and Engineering.

[24]  Daniel McDuff,et al.  Remote measurement of cognitive stress via heart rate variability , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.