# FF-YOLO: An Improved YOLO11-Based Fatigue Detection Algorithm for Air Traffic Controllers
## 2 Related Works  
In the field of fatigue monitoring, commonly used fatigue detection methods are primarily divided into subjective and objective categories. Subjective assessment methodsmainly rely on standardized questionnaires or scales, quantifying fatigue levels through self-reports from subjects. For example, [PERSON] et al. [8] proposed the Chalder Fatigue Scale (CFS). This scale is based on self-reported subjective reports and includes 14 items divided into two dimensions: physical fatigue and mental fatigue. The scale comprehensively covers both physical and mental fatigue, demonstrating high internal consistency and structural validity. [PERSON] et al. [12] introduced the NASA-TLX scale, originally designed to assess workload in aerospace tasks. [PERSON] et al. [13] applied the NASA-TLX scale to research on ATCO fatigue, illustrating the relationship between workload and ATCO fatigue. The NASA-TLX scale uses weighted scoring across six dimensions, namely, mental demand, physical demand, temporal demand, performance, effort, and frustration, thereby comprehensively reflecting the overall impact of task load on individuals. However, subjective scale methods rely on retrospective self-reports, which suffer from limitations such as time delays, recall biases, and differences in subjective perception. ATCOs engaged in high-intensity tasks cannot interrupt their work to fill out questionnaires, leading to delays in fatigue state detection. Additionally, individual differences in fatigue perception thresholds further diminish the objectivity of the results. Therefore, it is necessary to integrate objective detection methods to compensate for the shortcomings of subjective tools, enhancing the precision and reliability of real-time monitoring. To achieve real-time detection and eliminate individual subjective differences, objective methods are employed in fatigue detection. Objective methods can be categorized into intrusive and non-intrusive types. Intrusive methods primarily involve directly collecting human physiological signals to quantitatively assess fatigue states, including the detection of electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) signals. [PERSON] et al. [14] proposed a multi-branch deep network architecture (STFN-BRPS) based on EEG signals, addressing the shortcomings of existing methods in mining and fusing spatiotemporal EEG features, and achieved accurate recognition of driver fatigue states with an accuracy of 92.43%. [PERSON] et al. [15] utilized heart rate variability (HRV) features to overcome the deficiencies of traditional methods in distinguishing between blinking and drowsiness, effectively improving the accuracy and stability of fatigue state recognition. [PERSON] et al. [16] introduced a novel fatigue assessment framework that combines ECG and HRV features, extracting multi-scale features from ECG sequences and performing adaptive fusion to accurately detect fatigue during physical activity, achieving an accuracy of 94.0% and a recall rate of 89.3%. Intrusive methods based on physiological signals have also been applied in the situational awareness of ATCOs. [PERSON] et al. [17] employed a multi-modal approach using EEG and eye-tracking to reveal the intrinsic relationship between psychophysiological indicators and ATCOs' situational awareness, providing theoretical support for the development of real-time monitoring systems for air transportation safety. Although intrusive methods for objective fatigue monitoring can obtain high-precision physiological data, the equipment requires direct contact with the human body, causing discomfort that may reduce ATCOs' work efficiency or even lead to psychological resistance. Additionally, these methods are operationally complex and costly, making them difficult to deploy continuously in real-world scenarios. Therefore, non-intrusive methods, which use contactless technologies such as visual analysis to achieve continuous monitoring in natural states, are more practical.