Drowsiness Transitions Detection Using a Wearable Device

Due to a reduction in reaction time and, consequently, the driver’s concentration, driving when fatigued has become an issue throughout time. Consequently, likelihood of having accident and it being fatal increases. In this work, we aim identify automatic method capable detecting drowsiness transitions by considering time, frequency, nonlinear domains heart rate variability. Therefore, methodology proposed considers multivariate statistical process control, using principal components analysis, with accelerometer variability extracted wearable device. Applying approach, was possible improve results achieved previous studies, where able remove points out-of-control due signal noise, drowsy transitions, classification. It is important note that are not influenced external noise. terms limitations, detect all some individuals, falls far short expectations. Regarding this, essential understand if there any pattern or similarity among participants which fails.