Affiliations: [a] School of Physics, Northeast Normal University, Changchun, Jilin, China | [b] Faculty of Physics, University of Science, Pyongyang, Democratic People’s Republic of Korea | [c] Academy of Ultramodern Science, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea | [d] Faculty of Communications, Kim Chaek University of Technology, Pyongyang, Democratic People’s Republic of Korea
Correspondence:
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Corresponding author: Shili Liang, School of Physics, Northeast Normal University, Changchun, Jilin 130024, China. E-mail: [email protected].
Abstract: BACKGROUND: The imaging photoplethysmography (iPPG) method is a non-invasive, non-contact measurement method that uses a camera to detect physiological indicators. On the other hand, wearing a mask has become essential today when COVID-19 is rampant, which has become a new challenge for heart rate (HR) estimation from facial videos recorded by a camera. OBJECTIVE: The aim is to propose an iPPG-based method that can accurately estimate HR with or without a mask. METHODS: First, the facial regions of interest (ROI) were divided into two sub-ROIs, and the original signal was obtained through spatial averaging with different weights according to the result of judging whether wearing a mask or not, and the CDF, which emphasizes the main component signal, was combined with the improved POS suitable for real-time HR estimation to obtain the noise-removed BVP signal. RESULTS: For self-collected data while wearing a mask, MAE, RMSE, and ACC were 1.09 bpm, 1.44 bpm, and 99.08%, respectively. CONCLUSION: Experimental results show that the proposed framework can estimate HR stably in real-time in both cases of wearing a mask or not. This study expands the application range of HR estimation based on facial videos and has very practical value in real-time HR estimation in daily life.
