The Role of Artificial Intelligence in the Fatigue Risk Management System
Authors: Ziakkas Dimitrios , Keller Julius , Vhaduri Sudip 
Published: January 2024

Fatigue in aviation is defined as “a physiological state of reduced mental or physical performance capability resulting from sleep loss, extended wakefulness, circadian phase, and/or workload (mental and/or physical activity) that can impair a person’s alertness and ability to perform safety-related operational duties”. Fatigue compromises human performance and is considered an identified threat to flight safety. Fatigue is inevitable within the aviation operations context. Therefore, fatigue cannot be eliminated; it must be managed. Fatigue Risk Management System (FRMS) has recently been implemented in airline operations. FRMS is “a data-driven means of continuously monitoring and managing fatigue-related safety risks, based upon scientific principles, knowledge, and operational experience that aims to ensure relevant personnel are performing at adequate levels of alertness”. Nevertheless, studies reported prominent levels of fatigue for 68.5% to 93% among professional pilots. This implies that current FRMS strategies need to be strengthened. The ever-growing and constantly changing aviation industry mandates novel complementary approaches to enhance the established FRMS. EASA has already acknowledged the potential benefits of artificial intelligence (AI) in aviation. Implementing AI applications in FRMS could benefit both flight safety and cost efficiency. AI can analyze various sources of data to enhance real-time detection and prediction. Countermeasures could, then, be employed to address pilots’ drowsiness and mental impairments and, thus, prevent fatigue-related incidents. This study provides a holistic human-centric approach regarding AI utilization in FRMS by outlining the potential benefits and challenges. Implementing AI applications in FRMS could benefit both flight safety and cost efficiency. AI can be integrated with existing FRMS and provide a more comprehensive approach to fatigue management. Given AI’s computational capacity, it can analyze vast amounts of various sourced data to enhance real-time detection and prediction. Countermeasures could, then, be employed to address pilots’ drowsiness and mental impairment and, thus, prevent fatigue-related incidents. AI applications are promising in using pilot’s data sourced through wearable devices (e.g., smartwatches, fitness, and eye trackers) for the detection of fatigue in domains such as a) Monitoring/analyzing electroencephalogram (EEG) signal; b) Yawing detection; c) Facial muscle detection; d) Drowsiness detection; e) Pupil detection and monitor pilot fatigue levels in real-time. Additionally, AI could analyze natural language during the pilot’s communication to detect signs of fatigue. Also, AI could integrate and contribute to fatigue flight data. Moreover, AI can contribute to FRMS by identifying high-risk periods for fatigue and developing customized mitigation strategies based on individual characteristics such as circadian rhythms, sleep patterns, and workload. AI can be used to develop predictive models that can forecast the likelihood of fatigue for individual crew members based on numerous factors, such as sleep patterns and workload. These models can inform scheduling decisions and other fatigue risk management strategies. AI algorithms could also optimize crew rostering to avoid last-minute unfit-for-duty reports and prevent additional costs. On the contrary, there are considerable challenges to overcome. The reliability and validity of data used by AI systems are critical to their effectiveness in predicting and managing fatigue. Methods to measure the accuracy and consistency of data must be established and continually monitored. Additionally, there is a risk that AI systems can perpetuate or even amplify existing biases and inequalities based on training data. Regulations and legal frameworks must be established to guide the use of AI in fatigue risk management. EASA is working on a structured AI integration within the aviation industry. Personalized and sensitive data should be collected for the intended purpose. Data governance should ensure privacy and security to protect individuals. Also, some ethical considerations could be raised (e.g., the responsibility for decision-making and accountability in case of errors or incidents. Moreover, it is essential to understand how AI systems can integrate with human factors and not become a source of stress or distraction for pilots. Non-intrusive wearable AI systems’ sensor integration is of utmost importance. Implementing AI systems can be costly, and the return on investment must be carefully considered. Determining the cost-effectiveness of AI systems in fatigue risk management is essential. Lastly, resistance to change by the users could be anticipated. The Purdue research case study connects the ongoing measurement of Fatigue in aviation training (Purdue SATT student pilots) using AI applications with the commercial aviation FRMS