Remote photoplethysmography (rPPG) based learning fatigue detection.

Autor: Zhao, Liang, Zhang, Xinyu, Niu, Xiaojing, Sun, Jianwen, Geng, Ruonan, Li, Qing, Zhu, Xiaoliang, Dai, Zhicheng
Předmět:
Zdroj: Applied Intelligence; Dec2023, Vol. 53 Issue 23, p27951-27965, 15p
Abstrakt: Remote photoplethysmography (rPPG), which uses a facial video to measure skin reflection variations, is a contactless method for monitoring human cardiovascular activity. Due to its simplicity, convenience and potential in large-scale application, rPPG has gained more attention over the decade. However, the accuracy, reliability, and computational complexity have not reached the expected standards, thus rPPG has a very limited application in the educational field. In order to alleviate this issue, this study proposes an rPPG-based learning fatigue detection system, which consists of the following three modules. First, we propose an rPPG extraction module, which realizes real-time pervasive biomedical signal monitoring. Second, we propose an rPPG reconstruction module, which evaluates heart rate using a hybrid of 1D and 2D deep convolutional neural network approach. Third, we propose a learning fatigue classification module based on multi-source feature fusion, which classifies a learner's state into non-fatigue and fatigue. In order to verify the performance, the proposed system is tested on a self-collected dataset. Experimental results demonstrate that (i) the accuracy of heart rate evaluation is better than the cutting-edge methods; and (ii) based on both the subject-dependent and independent cross validations, the proposed system succeeded in not only learning person-independent features for fatigue detection but also detecting early fatigue with very high accuracy. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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