Image photoplethysmography can realize low-cost and easy-to-operate non-contact heart rate detection from the facial video, and effectively overcome the limitations of traditional contact method in daily vital sign monitoring. However, it is hard to obtain more accurate heart rate detection values under the conditions of subject’s facial movement, weak ambient light intensity and long detection distance, etc. In this article, a non-contact heart rate detection method based on face tracking is proposed, which can effectively improve the accuracy of non-contact heart rate detection method in practical application. The corner tracker algorithm is used to track the human face to reduce the motion artifact caused by the movement of the subject’s face and enhance the use value of the signal. And the maximum ratio combining algorithm is used to weight the pixel space pulse wave signal in the facial region of interest to improve the pulse wave extraction accuracy. We analyzed the facial images collected under different experimental distances and action states. This proposed method significantly reduces the error rate compared with the independent component analysis method. After theoretical analysis and experimental verification, this method effectively reduces the error rate under different experimental variables and has good consistency with the heart rate value collected by the medical physiological vest. This method will help to improve the accuracy of non-contact heart rate detection in complex environments.
References
[1]
Wang, W., Brinker, B.D., Stuijk, S. and Haan, G.D. (2016) Algorithmic Principles of Remote-PPG. IEEE Transactions on Biomedical Engineering, 99, 1479-1491. https://doi.org/10.1109/TBME.2016.2609282
[2]
Lu, G., Yang, F., Jing, X., Yu, X., Zhang, H., Xue, H., et al. (2011) Contact-Free Monitoring of Human Vital Signs via a Microwave Sensor. 5th International Conference on Bioinformatics and Biomedical Engineering, Wuhan, 10-12 May 2011, 1-3. https://doi.org/10.1109/icbbe.2011.5781497
[3]
Brueser, C., Hoog Antink, C., Wartzek, T., Walter, M. and Leonhardt, S. (2015) Ambient and Unobtrusive Cardiorespiratory Monitoring Techniques. IEEE Reviews in Biomedical Engineering, 8, 30-43. https://doi.org/10.1109/RBME.2015.2414661
[4]
Poh, M.Z., Mcduff, D.J. and Picard, R.W. (2010) Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam. IEEE Transactions on Biomedical Engineering, 58, 7-11. https://doi.org/10.1109/TBME.2010.2086456
[5]
Sun, Y. and Thakor, N. (2015) Photoplethysmography Revisited: From Contact to Noncontact, from Point to Imaging. IEEE Transactions on Bio-Medical Engineering, 63, 463-477. https://doi.org/10.1109/TBME.2015.2476337
[6]
Sun, Y., Papin, C., Azorin-Peris, V., Kalawsky, R., Greenwald, S. and Hu, S. (2012) Use of Ambient Light in Remote Photoplethysmographic Systems: Comparison between a High-Performance Camera and a Low-Cost Webcam. Journal of Biomedical Optics, 17, Article ID: 037005. https://doi.org/10.1117/1.JBO.17.3.037005
[7]
Moço, A.V., Stuijk, S. and Haan, G.D. (2016) Motion Robust PPG-Imaging through Color Channel Mapping. Biomedical Optics Express, 7, 1737-1754. https://doi.org/10.1364/BOE.7.001737
[8]
Verkruysse, W., Svaasand, L.O. and Nelson, J.S. (2008) Remote Plethysmographic Imaging Using Ambient Light. Optics Express, 16, 21434-21445. https://doi.org/10.1364/OE.16.021434
[9]
Poh, M.Z., Mcduff, D.J. and Picard, R.W. (2010) Non-Contact, Automated Cardiac Pulse Measurements Using Video Imaging and Blind Source Separation. Optics Express, 18, 10762-10774. https://doi.org/10.1364/OE.18.010762
[10]
De Haan, G. and Jeanne, V. (2013) Robust Pulse Rate from Chrominance-Based RPPG. IEEE Transactions on Biomedical Engineering, 60, 2878-2886. https://doi.org/10.1109/TBME.2013.2266196
[11]
Kumar, M., Veeraraghavan, A. and Sabharwal, A. (2015) Distance-PPG: Robust Non-Contact Vital Signs Monitoring Using a Camera. Biomedical Optics Express, 6, 1565-1588. https://doi.org/10.1364/BOE.6.001565
[12]
Mstafa, R.J. and Elleithy, K.M. (2016) A Video Steganography Algorithm Based on Kanade-Lucas-Tomasi Tracking Algorithm and Error Correcting Codes. Multimedia Tools and Applications, 75, 10311-10333. https://doi.org/10.1007/s11042-015-3060-0
[13]
Macwan, R., Benezeth, Y. and Mansouri, A. (2018) Remote Photoplethysmography with Constrained ICA Using Periodicity and Chrominance Constraints. BioMedical Engineering OnLine, 17, 22. https://doi.org/10.1186/s12938-018-0450-3
[14]
Lee, H.K., Choi, K.W., Kong, D. and Won, J. (2013) Improved Kanade-Lucas-Tomasi Tracker for Images with Scale Changes. 2013 IEEE International Conference on Consumer Electronics, Las Vegas, 11-14 January 2013, 33-34.
[15]
He, F., Man, H. and Wang, W. (2011) Maximal Ratio Diversity Combining Enhanced Security. IEEE Communications Letters, 15, 509-511. https://doi.org/10.1109/LCOMM.2011.030911.102343
[16]
Viola, P., Jones, M. and Snow, D. (2003) Detecting Pedestrians Using Patterns of Motion and Appearance. Proceedings Ninth IEEE International Conference on Computer Vision, Nice, 13-16 October 2003, Vol. 2, 734-741. https://doi.org/10.1109/ICCV.2003.1238422
