Automatic Driver Drowsiness Detection Using Haar Algorithm and Support Vector Machine Techniques

2015 ◽  
Vol 8 (2) ◽  
pp. 149-157 ◽  
Author(s):  
Ghassan Jasim AL-Anizy ◽  
Md. Jan Nordin ◽  
Mohammed M. Razooq
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Drowsiness Detection ◽  
Driver Drowsiness

scholarly journals EEG signal classification for drowsiness detection using wavelet transform and support vector machine

2021 ◽  
Vol 10 (2) ◽  
pp. 501
Author(s):  
Novie Theresia Br. Pasaribu ◽  
Timotius Halim ◽  
Ratnadewi Ratnadewi ◽  
Agus Prijono
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Support Vector ◽  
Eeg Signal ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Electroencephalogram Eeg ◽  
Behavioral Methods ◽  
Physiological Method ◽  
Fold Cross Validation

<span id="docs-internal-guid-ed628156-7fff-8934-2369-94f011b043ca"><span>There are several categories to detect and measure driver drowsiness such as physiological methods, subjective methods and behavioral methods. The most objective method for drowsiness detection is the physiological method. One of the physiological methods used is an electroencephalogram (EEG). In this research wavelet transform is used as a feature extraction and using support vector machine (SVM) as a classifier. We proposed an experiment of retrieval data which is designed by using modified-EAR and EEG signal. From the SVM training process, with the 5-fold cross validation, Quadratic kernel has the highest accuracy 84.5% then others. In testing Driving-2 process 7 respondents were detected as drowsiness class, and 3 respondents were detected as awake class. In the testing of Driving-3 process, 6 respondents were detected as drowsiness class, and 4 respondents were detected as awake class. </span></span>

scholarly journals Non-Invasive Driver Drowsiness Detection System

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4833
Author(s):  
Hafeez Ur Rehman Siddiqui ◽  
Adil Ali Saleem ◽  
Robert Brown ◽  
Bahattin Bademci ◽  
Ernesto Lee ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Ultra Wideband ◽  
Gradient Boosting ◽  
Support Vector ◽  
Drowsiness Detection ◽  
Non Invasive ◽  
Driver Drowsiness ◽  
Uwb Radar ◽  
Tree Classifier

Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death. Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar. Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy). Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%. This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration.

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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