Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam

We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an index for cardiac autonomic activity) were subsequently quantified and compared to corresponding measurements using Food and Drug Administration-approved sensors. High degrees of agreement were achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine.

[1]  P. Laguna,et al.  Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions , 2010, Physiological measurement.

[2]  Ioannis T. Pavlidis,et al.  Thermistor at a Distance: Unobtrusive Measurement of Breathing , 2010, IEEE Transactions on Biomedical Engineering.

[3]  Yue Tian,et al.  Contact-free Measurement of Heart Rate Variability via a Microwave Sensor , 2009, Sensors.

[4]  Masayuki Ishihara,et al.  Development of Non-contact Monitoring System of Heart Rate Variability (HRV) - An Approach of Remote Sensing for Ubiquitous Technology - , 2009, HCI.

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

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

[7]  John Allen Photoplethysmography and its application in clinical physiological measurement , 2007, Physiological measurement.

[8]  Michael L. Jacobson,et al.  Time-frequency analysis of heart rate variability , 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

[9]  Ben J. A. Kröse,et al.  EM detection of common origin of multi-modal cues , 2006, ICMI '06.

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

[11]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  E. Oja,et al.  Independent Component Analysis , 2001 .

[13]  E. F. Greneker,et al.  Radar sensing of heartbeat and respiration at a distance with applications of the technology , 1997 .

[14]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[15]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[16]  D. Eckberg,et al.  Important influence of respiration on human R-R interval power spectra is largely ignored. , 1993, Journal of applied physiology.

[17]  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.

[18]  W. Zijlstra,et al.  Absorption spectra of human fetal and adult oxyhemoglobin, de-oxyhemoglobin, carboxyhemoglobin, and methemoglobin. , 1991, Clinical chemistry.

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

[20]  M. Grigioni,et al.  Optical Vibrocardiography: A Novel Tool for the Optical Monitoring of Cardiac Activity , 2006, Annals of Biomedical Engineering.

[21]  Jean-Franois Cardoso High-Order Contrasts for Independent Component Analysis , 1999, Neural Computation.

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