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Driver cognitive fatigue can significantly affect driving and may lead to fatal accidents. In this regard, automatic detection of underload driver cognitive fatigue based on upper body posture dynamics is studied in this paper, where a semi-supervised approach is developed to identify the cognitive fatigue patterns of driver posture. Initially, an unsupervised Gaussian Mixture Model (GMM) clustering is applied to the acceleration data representing the driver's head, neck, and sternum obtained in a simulated driving through a motion capture suit. This provides the optimum clusters of the most-similar and correlated time-series data of driver upper posture. Then, an automatic labelling algorithm is developed that mines the maximal value and the standard deviation of each GMM cluster and assigns a symbol according to the discrepancy in postural behaviour. Finally, novel machine learning supervised classifiers, including Gaussian Support Vector Machines, and Bootstrap-Aggregating base ....
Driver mental fatigue is a major influential factor that results in inattentive driver condition ultimately to fatal crashes. In this paper, the driver mental fatigue patterns based on the driver's body postural behaviour are identified using a novel adaptive pattern recognition technique. The experiments were conducted on 20 healthy subjects in a MATHWORKS simulated driving environment. The posture of the driver was measured using the XSENS motion capture system. To monitor the actions performed under the influence of mental fatigue, variations in the acceleration of the head, neck, and sternum were extracted and deployed in an unsupervised manner. A fully adaptive version of the symbolic aggregate approximation algorithm based on unsupervised clustering was developed that identifies the time-series patterns of driver fatigue posture. The time-variant fatigue patterns were dynamically segmented and symbolized according to the discrepancy in the postural behaviour. The experimenta ....
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