Adapting remote photoplethysmography for Indonesian subjects: an examination of diverse rPPG techniques


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



vital signs, heart beat, RGB image, POS, rPPG


Vital sign measurements are essential in intensive care patients, such as in the ICU or emergency department, and also for newborns or prenatal babies. The duty nurse usually monitors these vital signs by manually writing down the patient's condition on a large piece of paper in front of the patient's room. The lack of nurses can hinder the process of monitoring patient vital signs. However, since the COVID-19 pandemic, people have limited contact with their surroundings, making measuring vital signs with contact uncomfortable and unhygienic. The typical non-contact method for measuring heart rate is the remote photoplethysmography (rPPG) technique. In this study, we proposed to assess the performance of various rPPG algorithms on the Indonesian subjects dataset. The algorithms used are CHROM, GREEN, ICA, LGI, PBV, PCA, and POS on 70 pieces of data. Based on the test results with three 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, P. A. Cameron, A. Mori, A. Maini, M. Fitzgerald, E. Paul, and A. Street, “Early prediction of acute traumatic coagulopathy,” Resuscitation, vol. 82, no. 9, pp. 1208–1213, Sep. 2011.

M. Weenk, H. van Goor, B. Frietman, L. J. Engelen, C. J. van Laarhoven, J. Smit, and T. H. van de Belt, “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.

P. Griffiths, A. Recio?Saucedo, C. Dall'Ora, J. Briggs, A. Maruotti, and P. Meredith, “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.

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.

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.

A. H. Kadish, “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.

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.

E. Kaniusas, “Biomedical Signals and Sensors I: Linking physiological phenomena and biosignals,” Springer Berlin Heidelberg, pp. 183-282, Jan. 2012.

H. P. Loveday, “epic3: national evidence-based guidelines for preventing healthcare-associated infections in NHS hospitals in England,” J. Hosp. Infect., vol. 86, no. 1, pp. S1-70, Jan. 2014.

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.

D. McDuff, “Camera Measurement of Physiological Vital Signs,” ACM Comput. Surv., vol. 55, no. 9, pp. 1–40, Jan. 2023.

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.

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.

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.

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, in CVPR ’13. USA: IEEE Computer Society, Jun. 2013, pp. 3430–3437.

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.

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.

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.

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.

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.

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.

G. Heusch, A. Anjos, and S. Marcel, “A Reproducible Study on Remote Heart Rate Measurement,” ArXiv, 2017.

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.

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.

X. Li, “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.

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.

M.-Z. Poh, D. J. McDuff, and R. W. Picard, “Non-contact, automated cardiac pulse measurements using video imaging and blind source separation,” Opt. Express, vol. 18, no. 10, pp. 10762–10774, May 2010.

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.