Key result
A feature fusion framework using ECG-PPG physiological features (LFDM) and XGBoost classification achieved approximately 97.2% accuracy for high cognitive load detection.
Why the study?
Assessing cognitive load is significant for judging learners' learning state and improving learning and teaching effects.
Does a feature fusion framework using ECG-PPG and XGBoost improve the accuracy of high cognitive load detection in learners compared to conventional approaches?
Does a feature fusion framework using ECG-PPG and XGBoost improve the accuracy of high cognitive load detection in learners compared to conventional approaches?
A novel feature fusion framework using ECG and PPG signals with XGBoost classification achieves high accuracy in detecting cognitive load.
Hypothesis-generating for wearable cognitive load monitoring in education; requires prospective clinical validation before adoption.
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.
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Wang et al. (2019) studied Cognitive load. ECG-PPG physiological feature fusion (LFDM) and XGBoost classification vs. Conventional approaches using HRV or PRV only, and other algorithms was evaluated on Accuracy of high cognitive load detection. A feature fusion framework using ECG-PPG physiological features (LFDM) and XGBoost classification achieved approximately 97.2% accuracy for high cognitive load detection.
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