Abstract
Estimating Heart Rate (HR) value is one of the great important things to determine a person’s physiological data and monitor the physiological signs. Nowadays, many kinds of research have demonstrated that the most physiological data “e.g., heart rate and respiration rate” can be accurately measured remotely and collected from photoplethysmographic (PPG) signals via digital cameras or webcams. The PPG signal will be subjected to several signals processing algorithms such as EMD, PCA/ICA, FFT, and others, in order to evaluate and extract the appropriate signs that help to extract the vital parameters. Furthermore, many related works have been made to improve the detection of this non-contact technique with the same result measured using contact sensors. In this paper, we present a general approach with familiar processing signal algorithms and their uses to extract heart rate values. Indeed, the current paper is mainly based on three commons signal processing algorithms widely used in the related field, which are the Empirical Mode Decomposition for a signal decomposition, Principal Component Analysis for the signal’s dimensionality reduction, moving average algorithm for signal denoising, and Fourier Transform for converting the PPG signal from the time domain into to frequency domain. Our proposed heart rate estimation systems show a good correlation relationship and closeness with a mean of error around of 1.59.
Supported by organization Laboratory of Systems Engineering and Information Technology LiSTi.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
DA COSTA, German. Optical remote sensing of heartbeats. Optics Communications, vol. 117, no 5--6, pp. 395-398 (1995)
Rahman, H., Ahmed, M.U., Begum, S.: Noncontact physiological parameters extraction using camera. In: The 1st Workshop on Embedded Sensor Systems for Health through Internet of Things (ESS-H IoT), Oct., (2015)
Bella, A., Latif, R., Saddik, A., Guerrouj, F. Z.: Monitoring of physiological signs and their impact on the Covid-19 pandemic. In: E3S Web of Conferences (Vol. 229, p. 01030) (2021). EDP Sciences
Bella, A., Latif, R., Saddik, A., Jamad, L.: Review and evaluation of heart rate monitoring based vital signs, a case study: covid-19 pandemic. In: 2020 6th IEEE Congress on Information Science and Technology (CiSt), pp. 79--83. IEEE (2021)
Lewandowska, M., Ruminski, J., Kocejko, T.: Measuring pulse rate with a webcam - a non-contact method for evaluating cardiac activity. In: Proceedings of the 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 405--410 (2011)
Balakrishnan, G., Durand, F., Guttag, J.: Detecting pulse from head motions in video. In: Proceedings of the 2013 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3430--3437 (2013)
Irani, R., Nasrollahi, K., Moeslund, T.B.: Improved pulse detection from head motions using DCT. In: Proceedings of the 9th International Conference on Computer Vision Theory and Applications, pp. 118--124 (2014)
Viola, P., Jones, M.: Robust real-time face detection, in computer vision, ICCV 2001. In: Proceedings of the Eighth IEEE International Conference on, pp. 747--747 (2001)
Mustafa, et Kader, W.A., Abdul, M.M.M.: A review of histogram equalization techniques in image enhancement application. J. Phys.: Conf. Ser. IOP Publishing, p. 012026 (2018)
Rouast, P.V., Adam, M.T.P., Chiong, R., et al.: Remote heart rate measurement using low-cost RGB face video: a technical literature review. Front. Comput. Sci. 12, 858–872 (2018)
Li, X., Chen, J., Zhao, G., Pietikainen, M.: Remote heart rate measurement from face videos under realistic situations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4264-4271) (2014)
Guo, Z., Wang, Z.J., Shen, Z.: Physiological parameter monitoring of drivers based on video data and independent vector analysis. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4374-4378). IEEE (2014)
Qi, H., Wang, Z. J., Miao, C.: Non-contact driver cardiac physiological monitoring using video data. In: 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP) (pp. 418-422). IEEE (2015)
Rahman, H., Ahmed, M.U., Begum, S.: Non-contact physiological parameters extraction using facial video considering illumination, motion, movement and vibration. IEEE Trans. Biomed. Eng. 67(1), 88–98 (2019)
Yadhuraj, S.R., Sudarshan, B.G., Prasanna Kumar, S.C.: GUI creation for removal of motion artifact in PPG signals. In: 2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 1--5). IEEE (2016)
Rareş, A., Reinders, M.J., Biemond, J.: Image sequence restoration in the presence of pathological motion and severe artifacts. In: 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (Vol. 4, pp. IV-3365). IEEE (2002)
Meding, K., Loktyushin, A., Hirsch, M.: Automatic detection of motion artifacts in MR images using CNNS. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 811-815). IEEE (2017)
Maurya, L., Kaur, P., Chawla, D., Mahapatra, P.: Non-contact breathing rate monitoring in newborns: a review. Comput. Biol. Med. 132, 104321 (2021)
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Liu, H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 454(1971) 903-995 (1998 )
Chen, X., Shao, J., Long, Y., Que, C., Zhang, J., Fang, J.: Identification of Velcro rales based on Hilbert-Huang transform. Phys. A: Stat. Mech. Appl. 401, 34–44 (2014)
Jolliffe, I.T., et CADIMA, J.: Principal component analysis: a review and recent developments. In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no 2065, p. 20150202 (2016)
Tarvainen, M.P., Ranta-Aho, P.O., Karjalainen, P.A.: An advanced detrending method with application to HRV analysis. IEEE Trans. Biomed. Eng. 49, 172–175 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
El khadiri, Z., Latif, R., Saddik, A. (2023). Remote Heart Rate Measurement Using Plethysmographic Wave Analysis. In: Aboutabit, N., Lazaar, M., Hafidi, I. (eds) Advances in Machine Intelligence and Computer Science Applications. ICMICSA 2022. Lecture Notes in Networks and Systems, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-031-29313-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-29313-9_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28845-6
Online ISBN: 978-3-031-29313-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)