Heart Rate based Fatigue Recognition for Human Factors Evaluation

Fatigue is one of the main factors that contribute to operator performance and maritime safety, making it important to develop fatigue recognition algorithms that can predict operator fatigue. In this paper. we propose a subject independent algorithm for fatigue recognition from heart rate using machine learning techniques for human factors evaluation. The final model with the Random Forest Classifier produced a mean classification accuracy of $67.2 \%$ for recognizing 2-levels of stress for unseen data. With a 1-minute data window for fatigue recognition updated every second, the proposed method could be applied for human factors evaluation including vessel traffic operators’ fatigue monitoring.