Optimization of Deep Neural Networks for Heartrate Estimation from Face Video Stream to Implement Smart Health-City 


Vol. 45,  No. 12, pp. 2220-2228, Dec.  2020
10.7840/kics.2020.45.12.2220


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  Abstract

Lots of studies are conducted to extract information about user’s condition from video streams instead of contact-based sensors, such as the estimation of heart rate from facial video streams. Heart rate provides various information about health conditions. In the past, it was extracted relying on traditional signal processing method detecting small movements of his/her face, However, recently researchers has conducted the heart rate estimation process using deep learning approach analyzing color changes of a face. In this paper, the heart rate is estimated from facial video streams using a deep learning model and a facial semantic segmentation method, which could remove motion artifacts, and only extracts skin information. Hyperparameters of the deep neural networks are optimized through an automatic approach, Bayesian Optimization and Hyperband

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  Cite this article

[IEEE Style]

G. Kim, Y. S. Hariyani, S. Han, H. Lee, R. Sohn, C. Park, "Optimization of Deep Neural Networks for Heartrate Estimation from Face Video Stream to Implement Smart Health-City," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 12, pp. 2220-2228, 2020. DOI: 10.7840/kics.2020.45.12.2220.

[ACM Style]

Gwangho Kim, Yuli Sun Hariyani, Seungwoo Han, HyeonBin Lee, Ryanghee Sohn, and Cheolsoo Park. 2020. Optimization of Deep Neural Networks for Heartrate Estimation from Face Video Stream to Implement Smart Health-City. The Journal of Korean Institute of Communications and Information Sciences, 45, 12, (2020), 2220-2228. DOI: 10.7840/kics.2020.45.12.2220.

[KICS Style]

Gwangho Kim, Yuli Sun Hariyani, Seungwoo Han, HyeonBin Lee, Ryanghee Sohn, Cheolsoo Park, "Optimization of Deep Neural Networks for Heartrate Estimation from Face Video Stream to Implement Smart Health-City," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 12, pp. 2220-2228, 12. 2020. (https://doi.org/10.7840/kics.2020.45.12.2220)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880