Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection
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 th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/4/943 |
| _version_ | 1811307398885801984 |
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| author | Sadegh Arefnezhad Sajjad Samiee Arno Eichberger Ali Nahvi |
| author_facet | Sadegh Arefnezhad Sajjad Samiee Arno Eichberger Ali Nahvi |
| author_sort | Sadegh Arefnezhad |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T09:03:42Z |
| format | Article |
| id | doaj.art-dcd48c43c2b945208854a446df76dcdf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T09:03:42Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-dcd48c43c2b945208854a446df76dcdf2022-12-22T02:53:02ZengMDPI AGSensors1424-82202019-02-0119494310.3390/s19040943s19040943Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature SelectionSadegh Arefnezhad0Sajjad Samiee1Arno Eichberger2Ali Nahvi3Institute of Automotive Engineering, Mechanical Engineering Department, Graz University of Technology, Graz 8010, AustriaInstitute of Automotive Engineering, Mechanical Engineering Department, Graz University of Technology, Graz 8010, AustriaInstitute of Automotive Engineering, Mechanical Engineering Department, Graz University of Technology, Graz 8010, AustriaMechanical Engineering Department, K.N. Toosi University of Technology, Tehran 19991-43344, IranThis 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.https://www.mdpi.com/1424-8220/19/4/943adaptive neuro-fuzzy inference system (ANFIS)driver drowsiness detectionfeature selectionparticle swarm optimization (PSO) |
| spellingShingle | Sadegh Arefnezhad Sajjad Samiee Arno Eichberger Ali Nahvi Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection Sensors adaptive neuro-fuzzy inference system (ANFIS) driver drowsiness detection feature selection particle swarm optimization (PSO) |
| title | Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection |
| title_full | Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection |
| title_fullStr | Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection |
| title_full_unstemmed | Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection |
| title_short | Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection |
| title_sort | driver drowsiness detection based on steering wheel data applying adaptive neuro fuzzy feature selection |
| topic | adaptive neuro-fuzzy inference system (ANFIS) driver drowsiness detection feature selection particle swarm optimization (PSO) |
| url | https://www.mdpi.com/1424-8220/19/4/943 |
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