Mobile QR Code QR CODE : The Transactions P of the Korean Institute of Electrical Engineers
The Transactions P of the Korean Institute of Electrical Engineers

Korean Journal of Air-Conditioning and Refrigeration Engineering

ISO Journal TitleTrans. P of KIEE
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    Korea Citation Index(KCI)

References

1 
Han, D., Bashar, S. K., Lazaro, J., Ding, E., Whitcomb, C., McManus, D. D., and Chon, K. H, “Smartwatch PPG peak detection method for sinus rhythm and cardiac arrhythmia,” In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4310-4313, IEEE, July 2019.DOI
2 
Park, S., Kim, B. K., and Dong, S. Y, “Self-Supervised Rgb-Nir Fusion Video Vision Transformer Framework for Rppg Estimation,” IEEE Transactions on Instrumentation and Measurement, 71, 1-10, 2019.DOI
3 
Sarkar, P., & Etemad, A. “Self-supervised learning for ecg-based emotion recognition. In ICASSP 2020-2020 IEEE International Conference on Acoustics,” Speech and Signal Processing (ICASSP), pp. 3217-3221, IEEE, May 2020.DOI
4 
A. Ni, A. Azarang, and N. Kehtarnavaz, “A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods,” Sensors (Basel, Switzerland), vol. 21, no. 11, pp. 3719, 2021.DOI
5 
Y. R. Ji, S. Y. Lim, S. Y. Park, S. H. Kim, and S. H. Dong, “Deep Learning-based Real-time Heart Rate Measurement System Using Mobile Facial Videos,” Journal of Korea Multimedia Society, vol. 24, no. 11, pp. 1481-1491, 2021.DOI
6 
Q. Zhan, W. Wang, and G. de Haan, “Analysis of CNN- based remote-PPG to understand limitations and sensitivities,” Biomedical optics express, vol. 11, no. 3, pp. 1268-1283, 2020.DOI
7 
K.B. Jaiswal and T. Meenpal, “rPPG-FuseNet: Non-contact heart rate estimation from facial video via RGB/MSR signal fusion,” Biomedical Signal Processing and Control, vol. 78, 104002, 2022.DOI
8 
K.B. Jaiswal and T. Meenpal, “Heart rate estimation network from facial videos using spatiotemporal feature image,” Computers in Biology and Medicine, vol. 151, Part A, 106307, 2022.DOI
9 
M. Hu, X. Wu, X. Wang, Y. Xing, N. An, and P. Shi, “Contactless blood oxygen estimation from face videos: A multi-model fusion method based on deep learning,” Biomedical Signal Processing and Control, vol. 81, 104487, 2023.DOI
10 
C. Zhao, H. Wang, and Y. Feng, “MSSTNet: Multi-scale facial videos pulse extraction network based on separable spatiotemporal convolution and dimension separable attention,” Virtual Reality & Intelligent Hardware, vol. 5, no. 2, pp. 124-141, 2023.DOI
11 
T. Luguev, D. Seuß and J. -U. Garbas, “Deep Learning based Affective Sensing with Remote Photoplethysmography,” 2020 54th Annual Conference on Information Sciences and Systems (CISS), pp. 1-4, 2020.DOI
12 
W. Mellouk, and W. Handouzi, “CNN-LSTM for automatic emotion recognition using contactless photoplythesmographic signals,” Biomedical Signal Processing and Control, vol. 85, 104907, 2023.DOI
13 
W. Wang, A. C. den Brinker, S. Stuijk and G. de Haan, “Algorithmic Principles of Remote PPG,” in IEEE Transactions on Biomedical Engineering, vol. 64, no. 7, pp. 1479-1491, July 2017, doi: 10.1109/TBME.2016.2609282.DOI
14 
S. Bobbia, R. Macwan, Y. Benezeth, A. Mansouri, and J. Dubois, “Unsupervised skin tissue segmentation for remote photoplethysmography,” Pattern Recognition Letters, vol. 124, pp 82-90, 2017.DOI