Hand-over-face occlusion and distance adaptive heart rate detection based on imaging photoplethysmography and pixel distance in online learning

Abstract

In online learning, imaging photoplethysmography (iPPG) can extract biometric information from the students’ face video, which can be used for further analysis of learning status. However, the iPPG-based methods cannot detect the heart rate accurately when the face-camera distance changes or the subject's face is occluded by the hand (Hand-Over-Face, HOF). This paper proposes a distance-adaptive heart rate detection model based on chroma and pixel distance, which can detect heart rate accurately with face-camera distance changing in a certain range. The influence factors of distance are verified on the region of interest (ROI) of the image in the process of iPPG signal extraction, and the relationship model is established between the pixel distance characteristic parameters and the physiological parameters through the BP neural network. The experimental results show that the RMSE of the model proposed in this paper is 1.50 bpm, and the model determination coefficient is 0.9504, which can effectively correct the heart rate detection error caused by face-camera distance changes. This paper proposes a gesture segmentation method GROI combined with gesture recognition to obtain an effective face ROI when HOF occurs. The experimental results show that when the ROI is occluded severely by the gesture, the RMSE for heart rate detection is reduced by 1.11 bpm, which demonstrates the effectiveness of the algorithm.