# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 2 Literature Review
### Physiological Indicators
Machine Learning Workload Detection Models  
[PERSON] et al. [81] and [PERSON] et al. [82] utilized SVM for workload modeling. The former employs eye-tracking and the latter uses EEG data, both achieving satisfactory predictive outcomes. [PERSON] et al. [83] developed an ANN model based on functional nearinfrared spectroscopy (fNIRS) data and reached an accuracy of 70.02% in classifying the workload of processing wayfinding information into three classes. [PERSON] [84] extracted heart rate variability from facial videos, combined with vehicle dynamics parameters, and studied four machine learning algorithms, namely, logistic regression (LR), support vector machine (SVM), linear discriminant analysis (LDA), and neural network (NN), for classifying driver workload; the results show that LR achieved the highest accuracy, reaching 92% in binary classification. [PERSON], [PERSON] et al. [85] utilized recursive feature elimination (RFE), along with a stacking ensemble machine learning algorithm composed of SVM, RF, and LR, achieving 91.67% accuracy in a three-class setting. [PERSON] et al. [86] applied bagging and stacking ensemble techniques to the STEW dataset, including a stacking BLSTM consisting of eight learners, which achieved a classification accuracy of 97% in a three-class setting. The results from this study underscore that ensemble networks can enhance the accuracy beyond that of individual learners while reducing prediction variance. However, these models did not account for individual physiological differences, and the accuracy of the trained models was not validated on new individuals. To address the issue of poor model generalization, some studies have optimized machine learning methods to mine shared features among subjects. They have constructed cross-subject workload detection models to mitigate the impact of inter-subject variability and non-stationary characteristics on workload recognition results. [PERSON] et al. [87] proposed a cross-operator cognitive workload recognition method using frequency domain features combined with an SVM classifier. [PERSON] et al. [88] compared the cross-operator performance of different classifiers, demonstrating the superiority of CNNs in extracting shared features. [PERSON] et al. [89] demonstrated that normalization approaches relying on the choice of appropriate baselines help compensate for the effects of temporal variation and individual differences. [PERSON] et al. [90] developed a workload classification model based on CNN and adversarial domain generalization, incorporating an adversarial domain generalization module to enhance model robustness and generalization by extracting shared features. Another line of research addresses individual differences by clustering individual physiological characteristics to distinguish different physiological patterns. [PERSON] et al. [26] used K-means to cluster the data before classifying it with SVM. [PERSON] [91] used the fuzzy c-means clustering algorithm to differentiate the workload characteristics of different drivers and created personalized driver workload profiles (PDWP) to reflect the unique physiological responses of drivers, achieving good accuracy in workload classification.