# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 2 Literature Review
### Physiological Indicators  
EEG data record the brain's electrical activity, which results from ion movement in neurons during cognitive processes [59]. When neurons activate, the voltage across the neuronal cells changes, leading to fluctuations [60]. The signal is typically divided into several frequency bands based on the range of brainwave power, i.e., delta band, theta band, alpha band, beta band, and gamma band, with each band representing different aspects of brain activity [61]. [PERSON] and [PERSON] [62] showed that delta wave power escalates during complex endeavors. However, due to the low frequency of the delta band, the clarity and reliability of the signal within this range can be easily affected by various types of interference and artifacts. [PERSON] et al. [63] used a task engagement index to develop the first EEG-based adaptive system and identified the ratio beta/(alpha + theta) as the most sensitive. [PERSON] et al. [64] later confirmed the effectiveness of this index. [PERSON] [65] and [PERSON] [66] found that alpha band power increases with increasing workload, most prominently at the temporal and occipital lobes, but also at the frontal lobe during complex tasks [67]. [PERSON] et al. [61] used the average power of the alpha band measured from four EEG channels on the frontal lobes (AF3, AF4) and temporal lobes (T7, T8) as an indicator of task workload. Their research validated that with an increase in workload, the average power of the alpha band also increased. This method is less challenging in terms of EEG signal processing and is more feasible. ECG signals are sensitive to workload, especially within safety-critical dynamic systems like transportation [68]. Several studies have demonstrated a correlation between ECG responses and workload levels [69; 70; 71]. The heart rate (HR) and heart rate variability (HRV) of ECG signals represents the natural fluctuations in the intervals between heartbeats. The key time-domain metrics of HRV encompass the standard deviation of NN intervals (SDNN), the percentage of successive RR intervals that vary by over 50 ms (pNN50), heart rate (HR), and the root mean square of successive differences between RR intervals (RMSSD) [72]. The frequency-domain includes the absolute power of the ultra-low-frequency band (ULF), the very-low-frequency band (VLF), the low-frequency band (LF), the high-frequency band (HF), and the LF-to-HF power ratio (LF/HF) [73]. [PERSON] et al. [70] collected six ECG measures in both the time and frequency domains to classify drivers' workload levels. [PERSON]. [PERSON] [71] indicated that cardiovascular measures are well suited to indexing the different mental states of pilots, as well as their dynamic responses to variations in workload. Existing research has demonstrated some significant correlation between certain eye movement metrics and subjects' workload. [PERSON] et al. 's study [74] suggests that upon immediate hazard detection, there is an increase in fixation duration and subsequent adjustments in fixation frequency to accommodate changes in visual demand. [PERSON] [42] and [PERSON] et al. [75] also found that both fixation duration and frequency increase under high workload. [PERSON] et al. [76] and [PERSON] et al. [77] demonstrated a close relationship between pupil diameter and workload. In addition, ergonomic and psychological studies have demonstrated that body posture can serve as one of the indicators for workload assessment [53]. [PERSON] et al. [78] showed that behavioral features enhanced the performance of automatic stress detection systems using physiological features. [PERSON] et al. [79] demonstrated that body postures assumed to perform an activity are negatively affected by workload perception. [PERSON] [80] used body posture to build an SVM model to recognize workload during task processes, where the amount of body movement was positively correlated with the level of stress. Therefore, indicators related to body posture could potentially be used to assess the workload of pilots during tasks.