Distant Measurement of Plethysmographic Signal in Various Lighting Conditions Using Configurable Frame-Rate Camera

Open access


Videoplethysmography is currently recognized as a promising noninvasive heart rate measurement method advantageous for ubiquitous monitoring of humans in natural living conditions. Although the method is considered for application in several areas including telemedicine, sports and assisted living, its dependence on lighting conditions and camera performance is still not investigated enough. In this paper we report on research of various image acquisition aspects including the lighting spectrum, frame rate and compression. In the experimental part, we recorded five video sequences in various lighting conditions (fluorescent artificial light, dim daylight, infrared light, incandescent light bulb) using a programmable frame rate camera and a pulse oximeter as the reference. For a video sequence-based heart rate measurement we implemented a pulse detection algorithm based on the power spectral density, estimated using Welch’s technique. The results showed that lighting conditions and selected video camera settings including compression and the sampling frequency influence the heart rate detection accuracy. The average heart rate error also varies from 0.35 beats per minute (bpm) for fluorescent light to 6.6 bpm for dim daylight.


  • [1] Verkruysse, W., Svaasand, L.O., Nelson, J.S. (2008). Remote plethysmographic imaging using ambient light. Optics express, 16(26), 21434−21445.

  • [2] Poh, M.Z., McDuff, D.J., Picard, R.W. (2010). Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics express, 18(10), 10762−10774.

  • [3] Cardoso, J.F. (1999). High-order contrasts for independent component analysis. Neural Comput., 11(1), 157-192.

  • [4] Jeanne, V., Asselman, M., den Brinker, B., Bulut, M. (2013). Camera-based heart rate monitoring in highly dynamic light conditions. Connected Vehicles and Expo (ICCVE), 2013 International Conference on, 798−799.

  • [5] McDuff, D., Gontarek, S., Picard, R.W. (2014). Remote detection of photoplethysmographic systolic and diastolic peaks using a digital camera. IEEE Transactions on Biomedical Engineering, 61(12), 2948−2954.

  • [6] Mestha, L.K., Kyal, S., Xu, B., Lewis, L.E., Kumar, V. (2014). Towards continuous monitoring of pulse rate in neonatal intensive care unit with a webcam. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, 3817−3820.

  • [7] Couderc, J.P., Kyal, S., Mestha, L.K., Xu, B., Peterson, D.R., Xia, X., Hall, B. (2014). Pulse Harmonic Strength of facial video signal for the detection of atrial fibrillation. Computing in Cardiology Conference (CinC), 661−664.

  • [8] Couderc, J.P., Kyal, S., Mestha, L.K., Xu, B., Peterson, D.R., Xia, X., Hall, B. (2015). Detection of atrial fibrillation using contactless facial video monitoring. Heart Rhythm, 12(1), 195−201.

  • [9] Li, X., Chen, J., Zhao, G., Pietikainen, M. (2014). Remote heart rate measurement from face videos under realistic situations. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 4264−4271.

  • [10] Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M. (2012). A multimodal database for affect recognition and implicit tagging. IEEE Transactions on Affective Computing, 3(1), 42−55.

  • [11] Balakrishnan, G., Durand, F., Guttag, J. (2013). Detecting pulse from head motions in video. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 3430−3437.

  • [12] Tarassenko, L., Villarroel, M., Guazzi, A., Jorge, J., Clifton, D.A., Pugh, C. (2014). Non-contact video-based vital sign monitoring using ambient light and auto-regressive models. Physiological measurement, 35(5), 807.

  • [13] Sugita, N., Obara, K., Yoshizawa, M., Abe, M., Tanaka, A., Homma, N. (2015). Techniques for estimating blood pressure variation using video images. Engineering in Medicine and Biology Society (EMBC), 37th Annual International Conference of the IEEE, 4218−4221.

  • [14] MATLAB and Signal Processing Toolbox and Image Processing Toolbox, Release 2016a The MathWorks, Inc., Natick, Massachusetts, United States.

  • [15] Przystup, P., Bujnowski, A., Ruminski, J., Wtorek, J. (2013). A multisensor detector of a sleep apnea for using at home. Human System Interaction (HSI), The 6th International Conference on, 513−517.

  • [16] Sur, F., Grediac, M. (2014). Sensor noise measurement in the presence of a flickering illumination. Image Processing (ICIP), IEEE International Conference on, 1763−1767.

  • [17] Trzupek, M., Ogiela, M.R., Tadeusiewicz, R. (2011). Intelligent image content semantic description for cardiac 3D visualisations. Engineering Applications of Artificial Intelligence, 24(8), 1410−1418.

Metrology and Measurement Systems

The Journal of Committee on Metrology and Scientific Instrumentation of Polish Academy of Sciences

Journal Information

IMPACT FACTOR 2016: 1.598

CiteScore 2016: 1.58

SCImago Journal Rank (SJR) 2016: 0.460
Source Normalized Impact per Paper (SNIP) 2016: 1.228


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 15 15 12
PDF Downloads 6 6 5