Adapting Remote Photoplethysmography for Indonesian Subjects: An Examination of Diverse rPPG Techniques


  • Martin Clinton Tosima Manullang Institut Teknologi Sumatera
  • Istighfariza Aprini Institut Teknologi Sumatera


vital signs, heartbeat frequency, RGB images, POS, rPPG


Vital sign measurements are very important in intensive care patients such as in the ICU, or emergency department and also for newborns or babies born prematurely. Monitoring is usually done by the duty nurse by writing down the patient's condition manually on a large paper in front of the patient's room. The lack of nurses can hinder the process of monitoring patient vital signs. However, since the co-19 pandemic until now people have limited contact with the surrounding, causing the measurement of vital signs with contact to be uncomfortable. So in this study it is proposed to make a non-contact heart rate meter (Remote Photopletysmography) using RGB images. And the algorithms used are CHROM, GREEN, ICA, LGI, PBV, PCA and POS on 70 pieces of data.  Based on the test results with 3 types of evaluation metrics, namely MAE (Mean Absolute Error), RMSE (Root Mean Square Error) and Bland Altman, it is found that the measurement results with the best performance POS algorithm with a low prediction error rate with the resulting MAE value of 2.59 and RMSE of 4.65.


B. Mitra et al., “Early prediction of acute traumatic coagulopathy,” Resuscitation, vol. 82, no. 9, pp. 1208–1213, Sep. 2011, doi: 10.1016/j.resuscitation.2011.04.007. [Online]. Available:

M. Weenk et al., “Continuous Monitoring of Vital Signs Using Wearable Devices on the General Ward: Pilot Study,” JMIR Mhealth Uhealth, vol. 5, no. 7, p. e91, Jul. 2017, doi: 10.2196/mhealth.7208. [Online]. Available:

P. Griffiths et al., “The association between nurse staffing and omissions in nursing care: A systematic review,” J. Adv. Nurs., vol. 74, no. 7, pp. 1474–1487, Jul. 2018, doi: 10.1111/jan.13564. [Online]. Available:

J. D. Kingsley and A. Figueroa, “Acute and training effects of resistance exercise on heart rate variability,” Clin. Physiol. Funct. Imaging, vol. 36, no. 3, pp. 179–187, May 2016, doi: 10.1111/cpf.12223. [Online]. Available:

G. D. Clifford, I. Silva, J. Behar, and G. B. Moody, “Non-invasive fetal ECG analysis,” Physiol. Meas., vol. 35, no. 8, pp. 1521–1536, Aug. 2014, doi: 10.1088/0967-3334/35/8/1521. [Online]. Available:

A. H. Kadish et al., “ACC/AHA clinical competence statement on electrocardiography and ambulatory electrocardiography. A report of the ACC/AHA/ACP-ASIM Task Force on Clinical Competence (ACC/AHA Committee to Develop a Clinical Competence Statement on Electrocardiography and Ambulatory Electrocardiography),” J. Am. Coll. Cardiol., vol. 38, no. 7, pp. 2091–2100, Dec. 2001, doi: 10.1016/s0735-1097(01)01680-1. [Online]. Available:

Z. D. Goldberger and A. L. Goldberger, “Therapeutic ranges of serum digoxin concentrations in patients with heart failure,” Am. J. Cardiol., vol. 109, no. 12, pp. 1818–1821, Jun. 2012, doi: 10.1016/j.amjcard.2012.02.028. [Online]. Available:

E. Kaniusas, “Biomedical Signals and Sensors I: Linking physiological phenomena and biosignals,” Jan. 2012 [Online]. Available: [Accessed: May 24, 2023]

H. P. Loveday et al., “epic3: national evidence-based guidelines for preventing healthcare-associated infections in NHS hospitals in England,” J. Hosp. Infect., vol. 86 Suppl 1, pp. S1-70, Jan. 2014, doi: 10.1016/S0195-6701(13)60012-2. [Online]. Available:

W. Verkruysse, L. O. Svaasand, and J. S. Nelson, “Remote plethysmographic imaging using ambient light,” Opt. Express, vol. 16, no. 26, pp. 21434–21445, Dec. 2008, doi: 10.1364/oe.16.021434. [Online]. Available:

D. McDuff, “Camera Measurement of Physiological Vital Signs,” ACM Comput. Surv., vol. 55, no. 9, pp. 1–40, Jan. 2023, doi: 10.1145/3558518. [Online]. Available:

D. J. McDuff, J. R. Estepp, A. M. Piasecki, and E. B. Blackford, “A survey of remote optical photoplethysmographic imaging methods,” Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 2015, pp. 6398–6404, 2015, doi: 10.1109/EMBC.2015.7319857. [Online]. Available:

W. Wang, A. C. den Brinker, S. Stuijk, and G. de Haan, “Algorithmic Principles of Remote PPG,” IEEE Trans. Biomed. Eng., vol. 64, no. 7, pp. 1479–1491, Jul. 2017, doi: 10.1109/TBME.2016.2609282. [Online]. Available:

M.-Z. Poh, D. J. McDuff, and R. W. Picard, “Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 1, pp. 7–11, Jan. 2011, doi: 10.1109/TBME.2010.2086456. [Online]. Available:

G. Balakrishnan, F. Durand, and J. Guttag, “Detecting Pulse from Head Motions in Video,” in Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2013, pp. 3430–3437, doi: 10.1109/CVPR.2013.440 [Online]. Available: [Accessed: May 24, 2023]

M. Lewandowska, J. Rumi?ski, T. Kocejko, and J. Nowak, “Measuring pulse rate with a webcam — A non-contact method for evaluating cardiac activity,” in 2011 Federated Conference on Computer Science and Information Systems (FedCSIS), Sep. 2011, pp. 405–410 [Online]. Available:

G. de Haan and V. Jeanne, “Robust pulse rate from chrominance-based rPPG,” IEEE Trans. Biomed. Eng., vol. 60, no. 10, pp. 2878–2886, Oct. 2013, doi: 10.1109/TBME.2013.2266196. [Online]. Available:

G. de Haan and A. van Leest, “Improved motion robustness of remote-PPG by using the blood volume pulse signature,” Physiol. Meas., vol. 35, no. 9, pp. 1913–1926, Aug. 2014, doi: 10.1088/0967-3334/35/9/1913. [Online]. Available:

C. S. Pilz, S. Zaunseder, J. Krajewski, and V. Blazek, “Local Group Invariance for Heart Rate Estimation from Face Videos in the Wild,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Jun. 2018, pp. 1335–13358, doi: 10.1109/CVPRW.2018.00172 [Online]. Available:

X. Niu, H. Han, S. Shan, and X. Chen, “VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video,” arXiv [cs.CV], Oct. 11, 2018 [Online]. Available:

M. Soleymani, J. Lichtenauer, T. Pun, and M. Pantic, “A Multimodal Database for Affect Recognition and Implicit Tagging,” IEEE Transactions on Affective Computing, vol. 3, no. 1, pp. 42–55, Jan. 2012, doi: 10.1109/T-AFFC.2011.25. [Online]. Available:

G. Heusch, A. Anjos, and S. Marcel, “A Reproducible Study on Remote Heart Rate Measurement,” ArXiv, 2017 [Online]. Available: [Accessed: May 24, 2023]

S. Bobbia, R. Macwan, Y. Benezeth, A. Mansouri, and J. Dubois, “Unsupervised skin tissue segmentation for remote photoplethysmography,” Pattern Recognit. Lett., vol. 124, pp. 82–90, Jun. 2019, doi: 10.1016/j.patrec.2017.10.017. [Online]. Available:

R. Stricker, S. Müller, and H.-M. Gross, “Non-contact video-based pulse rate measurement on a mobile service robot,” in The 23rd IEEE International Symposium on Robot and Human Interactive Communication, Aug. 2014, pp. 1056–1062, doi: 10.1109/ROMAN.2014.6926392 [Online]. Available:

X. Li et al., “The OBF Database: A Large Face Video Database for Remote Physiological Signal Measurement and Atrial Fibrillation Detection,” in 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), May 2018, pp. 242–249, doi: 10.1109/FG.2018.00043 [Online]. Available:

G. Boccignone, D. Conte, V. Cuculo, A. D’Amelio, G. Grossi, and R. Lanzarotti, “An Open Framework for Remote-PPG Methods and Their Assessment,” IEEE Access, vol. 8, pp. 216083–216103, undefined 2020, doi: 10.1109/ACCESS.2020.3040936. [Online]. Available:

S. Shu, H. Liang, Y. Zhang, Y. Zhang, and Z. Yang, “Non-contact measurement of human respiration using an infrared thermal camera and the deep learning method,” Meas. Sci. Technol., vol. 33, no. 7, p. 075202, Jul. 2022, doi: 10.1088/1361-6501/ac5ed9. [Online]. Available: [Accessed: Feb. 22, 2023]