A physiological data‐driven model for learners' cognitive load detection using HRV‐PRV feature fusion and optimized XGBoost classification. (30th July 2019)

• Record Type: Journal Article
• Authors: Guo, Junqi; Dai, Yazhu; Wang, Chixiang; Wu, Hao; Xu, Tianyou; Lin, Kai
• Other Names: Wu Hao guestEditor.; Bie Rongfang guestEditor.; Pereira Charith guestEditor.; Rana Omer guestEditor.
• Is Part Of: Software, practice & experience. Volume 50:Number 11(2020)
• Page Start: 2046
• Page End: 2064
• Publication Date: 2019-07-30
• Subjects: classification -- cognitive load detection -- feature fusion -- HRV -- physiological data‐driven model -- PRV -- XGBoost; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2730
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: Due to the increasing attention to online learning, cognitive load has been recently considered as a crucial indicator for judging teenagers' learning state so as to improve both learning and teaching effects. However, some traditional cognitive load measurement methods such as subjective measurement are easily influenced by subjective sensation deviation of subjects. None of them can reflect the cognitive load of learners more precisely. Recently, machine learning–based data modeling has gained more importance in the scenarios of various smart wearables and Internet of things applications. Meanwhile, physiological signals have proven to contribute much to human health assessment. On the basis of the above considerations, this paper proposes a physiological data‐driven model for learners' cognitive load detection under the application of smart wearables. The model consists of four modules: physiological signal acquisition, signal preprocessing, heart rate variability and pulse rate variability feature fusion, and cognitive load classification through an optimized extreme gradient boosting classifier in which hyperparameters are adaptively tuned with sequential model–based optimization. Furthermore, we design an experimental paradigm for signal acquisition in a learning environment, and the experimental results demonstrate that the proposed model for cognitive load detection outperforms conventional approaches that only employ either heart rate variability or pulse rate variability for modeling. We also compare the effects of different feature fusion algorithms combined with different classification algorithms, which demonstrates that the proposed model achieves the highest accuracy of cognitive load detection due to its optimal combination of feature fusion and classification.