Creating a Framework for the Collection of Biometric and Environmental Data During Collegiate Flight Training

Abstract

The aviation industry has long recognized fatigue as a critical safety hazard, yet fatigue management strategies have predominantly focused on long-haul commercial operations (Olaganathan et al., 2021). This focus leaves significant gaps in General Aviation (GA) specifically the collegiate flight training sectors (Mendonca et al., 2021). The purpose of this ongoing research project is to investigate the psychophysiological experiences of flight students and certified flight instructors who operate within a rigorous academic environment that also includes high-stakes flight training. To advance our understanding of pilot performance, the researchers are collecting and analyzing high fidelity multimodal data. The current study employs a comprehensive sensor suite to capture the interaction between a pilot’s internal state and the physical environment of the training aircraft. The research utilizes biometric data such as heart rate (HR), electrocardiography (ECG), electrodermal activity (EDA), heart rate variability (HRV), and other data to monitor physiological responses. Concurrently, the team measures environmental factors such as noise, vibration, and temperature. This objective data is paired with subjective pre-flight and post-flight surveys to provide a more complete context for each training event. By integrating these distinct data streams, the research team can identify trends and patterns. The analysis further explores how physiological metrics fluctuate during various flight training activities. This paper will discuss how multimodal monitoring can be incorporated into flight training activities, lessons learned, and future research opportunities. Ultimately, the progression of this type of research will support the development of next-generation flight risk assessment, including Fatigue Risk Management Systems (FRMS) that are specifically tailored to the unique flying of collegiate aviation pilots.

Keywords: Aviation Fatigue, Stress, Heart Rate Variability, Whole-body Vibration, Cockpit Noise, Multimodal Sensor Fusion, Machine Learning

DOI: 10.54941/ahfe1007365