Background: The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness detection algorithm based on the analysis of the pulse rate variability generated by the heartbeat and validates the proposed method by comparing it with an objective indicator of sleepiness (PERCLOS). Methods: changes in alert conditions affect the autonomic nervous system (ANS) and therefore heart rate variability (HRV), modulated in the form of a wave and monitored to detect long-term changes in the driver’s condition using real-time control. Results: the performance of the algorithm was evaluated through an experiment carried out in a road vehicle. In this experiment, data was recorded by three participants during different driving sessions and their conditions of fatigue and sleepiness were documented on both a subjective and objective basis. The validation of the results through PER-CLOS showed a 63% adherence to the experimental findings. Conclusions: the present study con-firms the possibility of continuously monitoring the driver’s status through the detection of the ac-tivation/deactivation states of the ANS based on HRV. The proposed method can help prevent accidents caused by drowsiness while driving.
On-road detection of driver fatigue and drowsiness during medium-distance journeys
Salvati L.;D'amore M.;Pellegrino A.;Sena P.;Villecco F.
2021
Abstract
Background: The detection of driver fatigue as a cause of sleepiness is a key technology capable of preventing fatal accidents. This research uses a fatigue-related sleepiness detection algorithm based on the analysis of the pulse rate variability generated by the heartbeat and validates the proposed method by comparing it with an objective indicator of sleepiness (PERCLOS). Methods: changes in alert conditions affect the autonomic nervous system (ANS) and therefore heart rate variability (HRV), modulated in the form of a wave and monitored to detect long-term changes in the driver’s condition using real-time control. Results: the performance of the algorithm was evaluated through an experiment carried out in a road vehicle. In this experiment, data was recorded by three participants during different driving sessions and their conditions of fatigue and sleepiness were documented on both a subjective and objective basis. The validation of the results through PER-CLOS showed a 63% adherence to the experimental findings. Conclusions: the present study con-firms the possibility of continuously monitoring the driver’s status through the detection of the ac-tivation/deactivation states of the ANS based on HRV. The proposed method can help prevent accidents caused by drowsiness while driving.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
