Key result
A random forest classifier using a corrected heart rate variability feature set detected fatigue in urban railway transit drivers with an accuracy of 92.5% for binary classification.
Why the study?
Fatigue among urban railway transit drivers impairs performance and contributes to railway accidents, necessitating a robust detection system.
Observational (n=198)
A random forest classifier using HRV features from wearable PPG sensors can accurately detect fatigue in urban railway transit drivers, potentially improving operational safety.
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Supports HRV-based fatigue monitoring in transit drivers; leaves open prospective validation before operational adoption.
Jiao et al. (2022) conducted an observational in Fatigue (n=198). Machine learning classifiers using heart rate variability data was evaluated on Binary fatigue classification accuracy. A random forest classifier using a corrected heart rate variability feature set detected fatigue in urban railway transit drivers with an accuracy of 92.5% for binary classification.
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