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Vol. 11, No. 4, November 2026 (Article in Progress)

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Spectral Quality-Based Weighted Multi-ROI Fusion for Robust Imaging Photoplethysmography

https://doi.org/10.22219/kinetik.v11i4.2727
Salsabila Aurellia
Telkom University
Annisa Humairani
Telkom University
Muhammad Ammar Asyraf
Biomedical Engineering Study Program, School of Electrical Engineering, Telkom University, Bandung, Indonesia
Hilman Fauzi
Telkom University

Corresponding Author(s) : Salsabila Aurellia

salsaurell@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 11, No. 4, November 2026 (Article in Progress)

Abstract

Imaging photoplethysmography (iPPG) enables non-contact heart rate (HR) estimation from facial videos, yet its reliability is strongly affected by spatial and temporal signal variability. While many studies focus on improving signal extraction algorithms, the impact of explicit facial region selection and quality-aware fusion remains underexplored. This study systematically proposed a spectral quality-based weighted multi-ROI fusion method and investigates the role of facial region-of-interest (ROI) selection, and temporal quality gating within a controlled and interpretable iPPG framework. A classical pipeline is employed to isolate spatial and quality-related effects. Experiments were conducted on 41 subjects from the UBFC-rPPG dataset. The results demonstrate that quality-weighted multi-ROI fusion provides statistically significant and more consistent performance compared to single-ROI and simple averaging strategies. With moderate temporal gating (τ = 0.20), the proposed method improves estimation accuracy while maintaining high coverage (92.31%) and full subject inclusion. A stricter threshold (τ = 0.25) further reduces the mean absolute error to 2.89 bpm and increases correlation to 0.86, albeit with reduced window-level coverage (41%). These findings highlight a clear trade-off between numerical accuracy and data availability and emphasize the importance of jointly considering spatial reliability and temporal quality control for robust and interpretable iPPG-based heart rate monitoring in realistic real-world deployment scenarios.

Keywords

iPPG Weighted Fusion Facial ROIs Heart Rate Plane-Orthogonal-to-Skin
Aurellia, S., Annisa Humairani, Muhammad Ammar Asyraf, & Hilman Fauzi. (2026). Spectral Quality-Based Weighted Multi-ROI Fusion for Robust Imaging Photoplethysmography. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 11(4). https://doi.org/10.22219/kinetik.v11i4.2727
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References
  1. N. Alugubelli, H. Abuissa, and A. Roka, “Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability—What We Know and What Is Coming,” Nov. 01, 2022, MDPI. doi: 10.3390/s22228903.
  2. C. H. Cheng, K. L. Wong, J. W. Chin, T. T. Chan, and R. H. Y. So, “Deep learning methods for remote heart rate measurement: A review and future research agenda,” Sep. 01, 2021, MDPI. doi: 10.3390/s21186296.
  3. A. Gupta, A. G. Ravelo-García, and F. M. Dias, “Availability and performance of face based non-contact methods for heart rate and oxygen saturation estimations: A systematic review,” Jun. 01, 2022, Elsevier Ireland Ltd. doi: 10.1016/j.cmpb.2022.106771.
  4. X. Zheng, C. Zhang, H. Chen, Y. Zhang, and X. Yang, “Remote measurement of heart rate from facial video in different scenarios,” Measurement (Lond)., vol. 188, Jan. 2022, doi: 10.1016/j.measurement.2021.110243.
  5. J. S. Ryu, S. C. Hong, S. Liang, S. Il Pak, Q. Chen, and S. Yan, “A measurement of illumination variation-resistant noncontact heart rate based on the combination of singular spectrum analysis and sub-band method,” Comput. Methods Programs Biomed., vol. 200, Mar. 2021, doi: 10.1016/j.cmpb.2020.105824.
  6. P. Gupta, B. Bhowmick, and A. Pal, “MOMBAT: Heart rate monitoring from face video using pulse modeling and Bayesian tracking,” Comput. Biol. Med., vol. 121, Jun. 2020, doi: 10.1016/j.compbiomed.2020.103813.
  7. B. Zhang, H. Li, L. Xu, L. Qi, Y. Yao, and S. E. Greenwald, “Noncontact Heart Rate Measurement Using a Webcam, Based on Joint Blind Source Separation and a Skin Reflection Model: For a Wide Range of Imaging Conditions,” J. Sens., vol. 2021, 2021, doi: 10.1155/2021/9995871.
  8. V. A. A. van Es, R. G. P. Lopata, E. P. Scilingo, and M. Nardelli, “Contactless Cardiovascular Assessment by Imaging Photoplethysmography: A Comparison with Wearable Monitoring,” Sensors, vol. 23, no. 3, Feb. 2023, doi: 10.3390/s23031505.
  9. N. Ansari, P. Yogarajah, T. M. McGinnity, P. Vance, and A. Peace, “ChPOS: A Contactless and Continuous Method for Estimation of Heart Rate from Face,” in 2023 34th Irish Signals and Systems Conference (ISSC), 2023, pp. 1–6. doi: 10.1109/ISSC59246.2023.10162125.
  10. H. Lee, H. Ko, H. Chung, Y. Nam, S. Hong, and J. Lee, “Real-time realizable mobile imaging photoplethysmography,” Sci. Rep., vol. 12, no. 1, Dec. 2022, doi: 10.1038/s41598-022-11265-x.
  11. D. Qiao, A. H. Ayesha, F. Zulkernine, N. Jaffar, and R. Masroor, “ReViSe: Remote Vital Signs Measurement Using Smartphone Camera,” IEEE Access, vol. 10, pp. 131656–131670, 2022, doi: 10.1109/ACCESS.2022.3229977.
  12. S. Premkumar and D. J. Hemanth, “Intelligent Remote Photoplethysmography-Based Methods for Heart Rate Estimation from Face Videos: A Survey,” Sep. 01, 2022, MDPI. doi: 10.3390/informatics9030057.
  13. 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.
  14. B. Lokendra and G. Puneet, “AND-rPPG: A novel denoising-rPPG network for improving remote heart rate estimation,” Comput. Biol. Med., vol. 141, Feb. 2022, doi: 10.1016/j.compbiomed.2021.105146.
  15. D. McDuff, “Camera Measurement of Physiological Vital Signs,” ACM Comput. Surv., vol. 55, no. 9, Sep. 2023, doi: 10.1145/3558518.
  16. J. Zhang et al., “Channel attention pyramid network for remote physiological measurement,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-06107-5.
  17. S. N. Yu, C. S. Wang, and Y. P. Chang, “Heart Rate Estimation From Remote Photoplethysmography Based on Light-Weight U-Net and Attention Modules,” IEEE Access, vol. 11, pp. 54058–54069, 2023, doi: 10.1109/ACCESS.2023.3281898.
  18. A. Pai, A. Veeraraghavan, and A. Sabharwal, “HRVCam: robust camera-based measurement of heart rate variability,” J. Biomed. Opt., vol. 26, no. 02, Feb. 2021, doi: 10.1117/1.jbo.26.2.022707.
  19. A. Hassanpour and B. Yang, “Contactless Vital Sign Monitoring: A Review Towards Multi-Modal Multi-Task Approaches,” Aug. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/s25154792.
  20. H. Xiao, T. Liu, Y. Sun, Y. Li, S. Zhao, and A. Avolio, “Remote photoplethysmography for heart rate measurement: A review,” Feb. 01, 2024, Elsevier Ltd. doi: 10.1016/j.bspc.2023.105608.
  21. R. J. Lee, S. Sivakumar, and K. H. Lim, “Review on remote heart rate measurements using photoplethysmography,” Multimed. Tools Appl., vol. 83, no. 15, pp. 44699–44728, May 2024, doi: 10.1007/s11042-023-16794-9.
  22. S. Li, M. Elgendi, and C. Menon, “Optimal facial regions for remote heart rate measurement during physical and cognitive activities,” npj Cardiovascular Health, vol. 1, no. 1, Nov. 2024, doi: 10.1038/s44325-024-00033-7.
  23. N. Nguyen, L. Nguyen, H. Li, M. Bordallo López, and C. Álvarez Casado, “Evaluation of video-based rPPG in challenging environments: Artifact mitigation and network resilience,” Comput. Biol. Med., vol. 179, Sep. 2024, doi: 10.1016/j.compbiomed.2024.108873.
  24. M. Bondarenko, C. Menon, and M. Elgendi, “The role of face regions in remote photoplethysmography for contactless heart rate monitoring,” NPJ Digit. Med., vol. 8, no. 1, Dec. 2025, doi: 10.1038/s41746-025-01814-9.
  25. S. Bhutani, M. Elgendi, and C. Menon, “Preserving privacy and video quality through remote physiological signal removal,” Communications Engineering, vol. 4, no. 1, Dec. 2025, doi: 10.1038/s44172-025-00363-z.
  26. D. Kim, K. Lee, and C. B. Sohn, “Assessment of ROI selection for facial video-based rPPG,” Sensors, vol. 21, no. 23, Dec. 2021, doi: 10.3390/s21237923.
  27. A. Ni, A. Azarang, and N. Kehtarnavaz, “A review of deep learning-based contactless heart rate measurement methods,” Jun. 01, 2021, MDPI AG. doi: 10.3390/s21113719.
  28. A. Kiddle, H. Barham, S. Wegerif, and C. Petronzio, “Dynamic Region of Interest Selection in Remote Photoplethysmography: Proof-of-Concept Study,” JMIR Form. Res., vol. 7, 2023, doi: 10.2196/44575.
  29. U. Debnath and S. Kim, “A comprehensive review of heart rate measurement using remote photoplethysmography and deep learning,” Dec. 01, 2025, BioMed Central Ltd. doi: 10.1186/s12938-025-01405-5.
  30. 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.
  31. The MathWorks Inc., “vision.CascadeObjectDetector System Object,” 2026.
  32. M. Elgendi, I. Martinelli, and C. Menon, “Optimal signal quality index for remote photoplethysmogram sensing,” npj Biosensing, vol. 1, no. 1, Jun. 2024, doi: 10.1038/s44328-024-00002-1.
  33. 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, 2020, doi: 10.1109/ACCESS.2020.3040936.
  34. J. Kranjec, S. Beguš, G. Geršak, and J. Drnovšek, “Non-contact heart rate and heart rate variability measurements: A review,” 2014, Elsevier Ltd. doi: 10.1016/j.bspc.2014.03.004.
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