Applying neural network analysis on heart rate variability data to assess driver fatigue

► In this study we show heart rate variability which can be used as a passive means to quantify drowsiness. Frequency domain components of HRV is used for early detection of fatigue. ► We also designed a neural network based artificial intelligent algorithm which detects whether the given HRV signal is in alert of fatigue state. ► The frequency domain components of HRV were also used to distinguish between parasympathetic (HF) and sympathetic (LF) activities using LF/HF ratio. Statistical analysis was also performed to identify the difference between LF/HF ratio during alert and fatigue states. Long duration driving is a significant cause of fatigue related accidents on motorways. Fatigue caused by driving for extended hours can acutely impair driver’s alertness and performance. This papers presents an artificial intelligence based system which could detect early onset of fatigue in drivers using heart rate variability (HRV) as the human physiological measure. The detection performance of neural network was tested using a set of electrocardiogram (ECG) data recorded under laboratory conditions. The neural network gave an accuracy of 90%. This HRV based fatigue detection technique can be used as a fatigue countermeasure.