# WorkloadGPT: A Large Language Model Approach to Real-Time Detection of Pilot Workload
## 2 Literature Review
### Methods for Recognizing Workload
#### 2.1.2 Physiological Measurement  
[PERSON], [PERSON] et al. [6] established an artificial neural network (ANN) model to recognize workload using heart rate variability (HRV) and galvanic skin response (GSR), with an accuracy of 73%. [PERSON], [PERSON] et al. [25] developed a support vector machine (SVM) model with an accuracy of 69%, based on electroencephalography (EEG). Additionally, [PERSON], [PERSON] et al. [26] utilized a multimodal deep learning (MDL) network architecture, integrating information from EEG, electrocardiography (ECG), respiratory (RESP) data, and electrodermal activity (EDA). [PERSON], [PERSON] [27] used k-nearest neighbor (KNN), SVM, feedforward neural networks (FNNs), recurrent neural networks (RNNs), and random forest (RF) to classify three levels of workload for 33 drivers in a driving simulator. They found that combining eye-tracking data with physiological data significantly improved the classification accuracy. [PERSON], [PERSON] [28] developed a non-contact method to classify driver workload using physiological parameters extracted from facial video images and vehicular parameters from controller area networks. They employed four machine learning algorithms--logistic regression (LR), SVM, linear discriminant analysis (LDA), and neural networks (NNs)--for classification. They concluded that the classification accuracy of non-contact camera data was comparable to, and often better than, traditional sensor data, due to fewer artifacts. [PERSON], [PERSON] [29] developed a fuzzy neural network (FNN) model to assess pilot workload by integrating flight data with physiological data (including heart rate and respiratory rate). They achieved an accuracy of 75% in distinguishing different workload levels. Although these methods can identify workload, they also have certain limitations. For instance, measuring indicators such as ECG, EEG, and GSR may interfere with pilot performance. In contrast, low-interference monitoring techniques can assess workload with minimal disruption to the pilot. For example, [PERSON], [PERSON] investigated a methodology focused on remote evaluations of peripheral neurovegetative activity using thermal infrared imaging, where the emotional state classified from thermal signal analysis was compared with that recognized by a facial action coding system [30]. Additionally, eye-trackingdevices and pressure cushions can also represent low-interference monitoring technologies. [PERSON] and [PERSON] stated that eye-tracking devices can accurately record eye movements without disrupting tasks, making them effective for workload assessment [31]. While pressure cushions have not yet been used for workload detection, they have been widely applied in monitoring the cognitive state of drivers [32; 33; 34]. Under the category of low-interference devices, such as eye-tracking devices and pressure cushions, the following key metrics are commonly collected. The average fixation duration [35] and the number of fixations [36] are critical indicators of a pilot's attention and concentration; longer fixation durations and increased fixation counts may suggest higher cognitive demands as the pilot focuses more intently on specific tasks or instruments. Similarly, the number of saccades [37] and the saccadic amplitude [38] can indicate workload levels; higher counts and larger amplitudes may suggest that the pilot is scanning the environment more frequently or shifting focus between different cockpit displays, both of which are associated with cognitive load. The number of blinks [39], often inversely correlated with cognitive load, is another significant indicator. A lower blink frequency might indicate increased cognitive demands as the pilot maintains focus, while a higher blink frequency could suggest fatigue or reduced workload. Additionally, as the pilot processes more information under high mental load conditions, the pupil diameter [40] may dilate due to cognitive strain.