Author: Nosseir, A.; Remon, D.
Title: Integrating intrusive and non-intrusive techniques to detect real time drivers' fatigue Cord-id: 5bzvm4i9 Document date: 2021_1_1
ID: 5bzvm4i9
Snippet: In the current context of COVID-19 pandemic, teaching driving online for beginners on car driving simulators will be encouraging. Furthermore, detecting the trainees' fatigue states is important for the instructors to know what is trainees' conditions to give them the appropriate instructions in the right time.This work presents a system that collects data from the face using non-intrusive system and data from the heart using intrusive system. It combines the data of both systems with a machine
Document: In the current context of COVID-19 pandemic, teaching driving online for beginners on car driving simulators will be encouraging. Furthermore, detecting the trainees' fatigue states is important for the instructors to know what is trainees' conditions to give them the appropriate instructions in the right time.This work presents a system that collects data from the face using non-intrusive system and data from the heart using intrusive system. It combines the data of both systems with a machine learning algorithm. It compares between the accuracy of the K-NN and the SVM classifiers to differentiate between fatigue and non-fatigue. The SVM is better and has 80% accuracy. © 2021 IEEE.
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