Contactless Heartbeat Detection Using Image Processing
Keywords:
Artificial intelligence, Contactless heartbeat detection, Deep learning, Image processing, Motion artifacts, Non-invasive monitoring, Real-time signal processing, Remote Photoplethysmography (rPPG), Telemedicine, Thermal imagingAbstract
Contactless heartbeat detection using image processing is gaining significant attention as a non- invasive method for real-time heart rate monitoring. Traditional techniques such as Electrocardiograms (ECG) and pulse oximeters require direct skin contact, which can cause discomfort and raise hygiene concerns. In contrast, contactless methods particularly Remote Photoplethysmography (rPPG) and thermal imaging offer an alternative by detecting subtle changes in skin color or temperature caused by blood volume fluctuations, enabling heart rate estimation without physical contact. This paper explores the effectiveness of rPPG and thermal imaging in various environments and discusses the role of advanced image processing techniques, including deep learning-based signal extraction and noise reduction, in enhancing accuracy. Key challenges such as motion artifacts, lighting variations, and skin tone disparities are addressed through adaptive algorithms and machine learning models.
Furthermore, the study highlights the benefits of multi-sensor fusion integrating RGB, infrared, and depth data to improve robustness and reliability. The use of embedded platforms and AI-driven optimization is also reviewed, emphasizing their role in enabling real time performance and portability. Finally, potential future improvements are discussed, focusing on the integration of AI with wearable technology and telemedicine applications. These advancements aim to make contactless heart rate monitoring more accurate, accessible, and practical for real-world use.
References
A. Manni, A. Caroppo, G. Rescio, P. Siciliano, and A. Leone, “Benchmarking of contactless heart rate measurement systems in arm-based embedded platforms,” Sensors, vol. 23, no. 7, p. 3507, Mar. 2023. https://doi.org/10.3390/s23073507
M. A. Hassan, G. S. Malik, N. Saad, B. Karasfi, Y. S. Ali, and D. Fofi, “Optimal source selection for image photoplethysmography,” in Proc. 2016 IEEE Int. Instrum. Meas. Technol. Conf. (I2MTC), May 2016, pp. 1–5. https://doi.org/10.1109/I2MTC.2016.7520406
X. Li, J. Chen, G. Zhao, and M. Pietikäinen, “Remote heart rate measurement from face videos under realistic situations,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Columbus, OH, USA, Jun. 2014, pp. 4264–4271. https://doi.org/10.1109/CVPR.2014.543
D. McDuff, S. Gontarek, and R. W. Picard, “Improvements in remote cardiopulmonary measurement using a five band digital camera,” IEEE Trans. Biomed. Eng., vol. 61, no. 10, pp. 2593–2601, Oct. 2014. https://doi.org/10.1109/TBME.2014.2323695
D. Shao, Y. Yang, C. Liu, F. Tsow, H. Yu, and N. Tao, “Noncontact monitoring breathing pattern, exhalation flow rate and pulse transit time,” IEEE Trans. Biomed. Eng., vol. 61, no. 11, pp. 2760–2767, Nov. 2014. https://doi.org/10.1109/TBME.2014.2327024
M. Van Gastel, S. Stuijk, and G. De Haan, “New principle for measuring arterial blood oxygenation, enabling motion-robust remote monitoring,” Scientific Reports, vol. 6, no. 1, p. 38609, Dec. 2016. https://doi.org/10.1038/srep38609
L. Kong, Y. Zhao, L. Dong, Y. Jian, X. Jin, B. Li, Y. Feng, M. Liu, X. Liu, and H. Wu, “Non-contact detection of oxygen saturation based on visible light imaging device using ambient light,” Opt. Express, vol. 21, no. 15, pp. 17464–17471, Jul. 2013. https://doi.org/10.1364/OE.21.017464
A. Moço and W. Verkruysse, “Pulse oximetry based on photoplethysmography imaging with red and green light: Calibratability and challenges,” J. Clin. Monit. Comput., vol. 35, no. 1, pp. 123–133, Feb. 2021. https://doi.org/10.1007/s10877-019-00449-y
J. Fei and I. Pavlidis, “Thermistor at a distance: Unobtrusive measurement of breathing,” IEEE Trans. Biomed. Eng., vol. 57, no. 4, pp. 988–998, Apr. 2010. https://doi.org/10.1109/TBME.2009.2032415
J. Deng, J. Guo, E. Ververas, I. Kotsia, and S. Zafeiriou, “RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2020, pp. 5202–5211. https://doi.org/10.1109/CVPR42600.2020.00525
M. A. Fiedler, P. Werner, M. Rapczyński, and A. Al-Hamadi, “Deep face segmentation for improved heart and respiratory rate estimation from videos,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 7, pp. 9383–9402, Jul. 2023. https://link.springer.com/article/10.1007/s12652-023-04607-8
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. https://doi.org/10.1364/OE.16.021434
H. Y. Wu, M. Rubinstein, E. Shih, J. Guttag, F. Durand, and W. Freeman, “Eulerian video magnification for revealing subtle changes in the world,” ACM Trans. Graph. (TOG), vol. 31, no. 4, pp. 1–8, Jul. 2012. https://doi.org/10.1145/2185520.2185561
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, Aug. 2011. https://doi.org/10.1109/T-AFFC.2011.25
X. Yu, M. Paul, C. H. Antink, B. Venema, V. Blazek, C. Bollheimer, S. Leonhardt, and D. Teichmann, “Non-contact remote measurement of heart rate variability using near-infrared photoplethysmography imaging,” in Proc. 40th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), Jul. 18, 2018, pp. 846–849. https://doi.org/10.1109/EMBC.2018.8512451
T. Zhang, W. Zheng, Z. Cui, Y. Zong, J. Yan, and K. Yan, “A deep neural network-driven feature learning method for multi-view facial expression recognition,” IEEE Trans. Multimedia, vol. 18, no. 12, pp. 2528–2536, Aug. 2016. https://doi.org/10.1109/TMM.2016.2598092
R. M. Fouad, O. A. Omer, and M. H. Aly, “Optimizing remote photoplethysmography using adaptive skin segmentation for real-time heart rate monitoring,” IEEE Access, vol. 7, pp. 76513–76528, Jun. 2019. https://doi.org/10.1109/ACCESS.2019.2922304
