Exploring the role of cardiac activity in forecasting cognitive fatigue with machine learning

Fatigue poses significant risks to safety, productivity, and overall well-being. Traditional statistical methods have been employed for inferring fatigue-related patterns; however, there is a need for interpretable machine learning approaches that integrate time series prediction capabilities to develop intelligent health systems for fatigue management. This study explored forecasting perception and performance scores associated with fatigue using cardiac activity and heart rate variability (HRV), employing both generalized and personalized models with a 10-minute forecast interval during a cognitively fatiguing task. Participants underwent a two-hour working memory task while providing subjective fatigue responses every 10 min, serving as perception labels, and their performance within each 10-minute interval was quantified as their performance labels. The results revealed that performance-labeled models generated lower mean absolute errors compared to perception-labeled models, while the Gradient Boosting Regression algorithm achieved the lowest mean absolute error in forecasting performance scores due to fatigue for both generalized and personalized models. Sample entropy, ratio of Standard Deviation-Poincaré, the proportion of peak-to-peak intervals over 50 ms, coefficient of variation of peak-to-peak intervals, and low frequency were the most important features for predicting performance. These findings offer the potential to forecast performance decline resulting from fatigue in working memory tasks, facilitating the implementation of fatigue mitigation interventions to reduce injury risks and performance impairments. The integration of interpretable machine learning methods with time series forecasting provides valuable insights for developing intelligent systems that proactively manage fatigue and optimize performance across various domains.