Detection and Analysis of Driver Drowsiness
Driver drowsiness is one of the major causes of road accidents. An estimated number of 1.2 million crashes, 8,000 lost lives, 500,000 injuries annually relates to drowsy-driving. From the information provided by a number of statistical reports, it can be proven that a large number of fatalities is c...
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2018
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my-utp-utpedia.191472019-06-20T10:40:56Z http://utpedia.utp.edu.my/19147/ Detection and Analysis of Driver Drowsiness Amirudin, Nurul Aisyah Driver drowsiness is one of the major causes of road accidents. An estimated number of 1.2 million crashes, 8,000 lost lives, 500,000 injuries annually relates to drowsy-driving. From the information provided by a number of statistical reports, it can be proven that a large number of fatalities is caused by driver drowsiness. In earlier researches, to estimate the level of drowsiness, the measures to be focused on are single measures either vehicle-based, physiological measure, behavioural measure or subjective measure. This paper analyses two single measures which includes physiological and behavioural measures such as EEG signals and video sequences together. Features extracted from EEG signals are the Delta, Theta and Alpha frequency bands which includes Delta power ( 2018-09 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/19147/1/Dissertation.pdf Amirudin, Nurul Aisyah (2018) Detection and Analysis of Driver Drowsiness. UNSPECIFIED. |
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Driver drowsiness is one of the major causes of road accidents. An estimated number of 1.2 million crashes, 8,000 lost lives, 500,000 injuries annually relates to drowsy-driving. From the information provided by a number of statistical reports, it can be proven that a large number of fatalities is caused by driver drowsiness. In earlier researches, to estimate the level of drowsiness, the measures to be focused on are single measures either vehicle-based, physiological measure, behavioural measure or subjective measure. This paper analyses two single measures which includes physiological and behavioural measures such as EEG signals and video sequences together. Features extracted from EEG signals are the Delta, Theta and Alpha frequency bands which includes Delta power ( |
| format |
Final Year Project |
| author |
Amirudin, Nurul Aisyah |
| spellingShingle |
Amirudin, Nurul Aisyah Detection and Analysis of Driver Drowsiness |
| author_facet |
Amirudin, Nurul Aisyah |
| author_sort |
Amirudin, Nurul Aisyah |
| title |
Detection and Analysis of Driver Drowsiness |
| title_short |
Detection and Analysis of Driver Drowsiness |
| title_full |
Detection and Analysis of Driver Drowsiness |
| title_fullStr |
Detection and Analysis of Driver Drowsiness |
| title_full_unstemmed |
Detection and Analysis of Driver Drowsiness |
| title_sort |
detection and analysis of driver drowsiness |
| publishDate |
2018 |
| url |
http://utpedia.utp.edu.my/19147/1/Dissertation.pdf http://utpedia.utp.edu.my/19147/ |
| _version_ |
1739832594176933888 |
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13.160551 |