A remote PPG based approach for heart rate estimation
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https://doi.org/10.54939/1859-1043.j.mst.79.2022.31-40Keywords:
Heart rate estimation; Non-contact heart rate mornitoring; PPG; Face recognition; Real face detection, Camera-based contactless PPG.Abstract
Heart rate (HR) estimation is an essential physiological process in the field of biosignal analysis. Recently, remote Photoplethysmography (rPPG) is a pathbreaking development in this field wherein the PPG signal is extracted from non-contact face videos. This study, aiming at building a low cost rPPG system for both heart rate estimation and detecting real face, proposes a non-contact, automatic technique of heart rate measurement from video based on the signal processing techniques. The heart rate is remotely measured from a video recording of a person's face using the camera from a smartphone. At the same time, the heart rate extracted from the videos recorded by a smartphone camera is compared with the data obtained from the PPG optical absorption sensor and achieved a similar result. The system can estimate the heart rates from faces from single subject or multiple participants, and can be applied for anti spoofing face recognition.
References
[1]. Z. Hasan, S. R. Ramamurthy, and N. Roy, "CamSense: A camera-based contact-less heart activity monitoring," Smart Health, vol. 23, pp. 1-13, 2022.
[2]. J. M. Arnold, D. H. Fitchett, J. G. Howlett, E. M. Lonn, and J.-C. Tardif, "Resting heart rate: a modifiable prognostic indicator of cardiovascular risk and outcomes?," Canadian Journal of Cardiology, vol. 24, pp. 3A-8A, 2008.
[3]. M. Z. Poh, D. J. McDuff, and R. Ư. W. Picard, "Non-contact, automated cardiac pulse measurements using video imaging and blind source separation," Optics Express, vol. 18, pp. 10762-10774, 2010.
[4]. D. McDuff, S. Gontarek, and R. W. Picard, "Remote detection of photoplethysmographic systolic and diastolic peaks using a digital camera," IEEE Transactions on Biomedical Engineering, vol. 61, pp. 2948-2954, 2014.
[5]. C. Zhao, C. L. Lin, W. Chen, and Z. Li, "A novel framework for remote photoplethysmography pulse extraction on compressed videos" in Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 1299-1308.
[6]. H. Demirezen and C. E. Erdem, "Remote photoplethysmography using nonlinear mode decomposition," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 1060-1064.
[7]. S. Huynh, R. K. Balan, J. Ko, and Y. Lee, "VitaMon: measuring heart rate variability using smartphone front camera," in the 17th conference on embedded networked sensor systems, 2019, pp. 1-14.
[8]. Q. Zhan, W. Wang, and G. Haan, "Analysis of CNN-based remote-PPG to understand limitations and sensitivities," Biomedical Optics Express, vol. 11, pp. 1268-1283, 2020.
[9]. I. Pavlidis, N. L. Eberhardt, and J. Levine, "Human behavior: Seeing through the face of deception," Nature, vol. 415, pp. 35, 2002.
[10]. P.A. Viola, and M.J. Jones., "Robust real-time face detection," International Journal of Computer Vision volume vol. 57, pp. 137–154, 2004.
[11]. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, vol. 55, pp. 119–139, 1997.
