Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi

Abstract

Background: Drowsiness condition is one of the significant factors often encountered when an accident occurs. We aimed to detect a method to prevent accidents caused by drowsiness and lost a focused driver.
 Methods: The image processing technique has been capable of detecting the characteristic of drowsiness and lost focus driver in real-time using Raspberry Pi. Video samples were processed using the Haar Cascade Classifier method to identify areas of the face, eyes, and mouth so that drowsy conditions. The methods can be determined based on the bject detected.
 Results: Two parameters were determined, the lost focused and drowsiness driver. The highest accuracy value for driver lost focused detection was 88.00%, while the highest accuracy value for drowsiness driver detection was 90.40%.
 Conclusion: In general, a system developed with image processing methods has been able to monitor the drowsiness and lost focused drivers with high accuracy. This system still needs improvements to increase performance.

Evaluation of Driver Irritation using Deep Body Temperature Variability Analysis

“…Assessment of driver irritation is an important aspect that affects traffic safety and driving performance. Although continuous measurement of facial images and autonomic indices estimated from heartbeats [1][2][3][4][5] are used in automotive biometric measurements, these methods have drawbacks such as difficulty in privacy and noise removal. Therefore, in this study, we investigated the usefulness of continuous tympanic temperature measurement from its time-series data using deep body temperature variation analysis on a trial basis, and also verified whether it is possible to objectively evaluate driver irritation.…”