Investigating 24- to 48-hr Forecast of Offshore Worker Alertness and Vigilance Using Multimodal Sources: A Proactive Fatigue Monitoring Paradigm

Fatigue is a significant contributor to accidents in the high-risk oil and gas industry. This study developed and evaluated models for forecasting fatigue manifestation in offshore workers using the Psychomotor Vigilance Task (PVT). Seventy offshore workers participated in a four-week study, providing data on sleep, physiological, subjective, and performance measures. Various machine learning models (Ridge, Random Forest, Support Vector, Long Short-Term Memory (LSTM) regressions) were employed to predict PVT reaction times using different data normalization between generalized and personalized datasets. Results indicate that personalized Support Vector Regression models outperform other models in predicting short-term fatigue. Age and perceived exertion emerged as crucial predictors of fatigue. The findings underscore the potential of personalized fatigue forecasting for enhancing safety in the oil and gas industry.