Paper Info
Title
A data-driven framework for learners' cognitive load detection using ECG-PPG physiological feature fusion and XGBoost classification.
Abstract
Cognitive load condition is of great significance for judging learners’ learning state and improving the learning and teaching effects. This paper proposed a feature fusion based processing framework for high cognitive load detection, which includes heart rate variability (HRV) and pulse rate variability (PRV) acquisition, data preprocessing, feature extraction, feature selection, feature fusion by linear feature dependency modeling (LFDM) and high cognitive load detection by XGBoost classifier. This paper experiment on simulated learning paradigm, and the experimental results show that the proposed framework for detection of high cognitive load outperforms conventional processing approaches that uses HRV or PRV only. This paper compared the effects of using different feature fusion algorithms (PCT, SKRRR, ADMM, LFDM) and different classification algorithms (KNN, SVM, DT, RF, XGBoost), and the final proposed framework outperforms other schemes. The proposed framework achieves approximately 97.2% accuracy of high cognitive load detection.
Year
DOI
Venue
2018
10.1016/j.procs.2019.01.234
Procedia Computer Science
Keywords
Field
DocType
Cognitive load,HRV,PRV,physiological signal processing,feature fusion,classifier,LFDM,XGBoost
Data-driven,Feature selection,Computer science,Support vector machine,Data pre-processing,Feature extraction,Artificial intelligence,Classifier (linguistics),Cognitive load,Statistical classification,Machine learning
Conference
Volume
ISSN
Citations 
147
1877-0509
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Chixiang Wang100.68
Junqi Guo26115.07