Drowsiness Detection System for Pilots

2013 ◽  
pp. 991-1003 ◽  
Author(s):  
Gurpreet Singh ◽  
M. Manivannan
Keyword(s):  
Detection System ◽  
Drowsiness Detection

scholarly journals Development of a Drowsiness Detection System using a Histogram for Vehicle Safety

2015 ◽  
Vol 21 (2) ◽  
pp. 102-107 ◽  
Author(s):  
Su Min Kang ◽  
Kyung Moo Huh ◽  
Young-Bok Joo
Keyword(s):  
Detection System ◽  
Vehicle Safety ◽  
Drowsiness Detection

CNN-Based Driver Drowsiness Detection System

2021 ◽  
pp. 153-166
Author(s):  
Pritesh Kumar Singh ◽  
Mayank Upadhyay ◽  
Archit Gupta ◽  
Puneet Singh Lamba
Keyword(s):  
Detection System ◽  
Drowsiness Detection ◽  
Driver Drowsiness

scholarly journals Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 943 ◽  
Author(s):  
Sadegh Arefnezhad ◽  
Sajjad Samiee ◽  
Arno Eichberger ◽  
Ali Nahvi
Keyword(s):  
Feature Selection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection System ◽  
Steering Wheel ◽  
Support Vector ◽  
Inference System ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Neuro Fuzzy

This paper presents a novel feature selection method to design a non-invasive driver drowsiness detection system based on steering wheel data. The proposed feature selector can select the most related features to the drowsiness level to improve the classification accuracy. This method is based on the combination of the filter and wrapper feature selection algorithms using adaptive neuro-fuzzy inference system (ANFIS). In this method firstly, four different filter indexes are applied on extracted features from steering wheel data. After that, output values of each filter index are imported as inputs to a fuzzy inference system to determine the importance degree of each feature and select the most important features. Then, the selected features are imported to a support vector machine (SVM) for binary classification to classify the driving conditions in two classes of drowsy and awake. Finally, the classifier accuracy is exploited to adjust parameters of an adaptive fuzzy system using a particle swarm optimization (PSO) algorithm. The experimental data were collected from about 20.5 h of driving in the simulator. The results show that the drowsiness detection system is working with a high accuracy and also confirm that this method is more accurate than the recent available algorithms.

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