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Driver mental fatigue is considered a major factor affecting driver behavior that may result in fatal accidents. Several approaches are addressed in the literature to detect fatigue behavior in a timely manner through either physiological or in-vehicle measurement methods. However, the literature lacks the implementation of hybrid approaches that combine the strength of individual approaches to develop a robust fatigue detection system. In this regard, a hybrid temporal approach is proposed in this paper to detect driver mental fatigue through the combination of driver postural configuration with vehicle longitudinal and lateral behavior on a study sample of 34 diverse participants. A novel fully adaptive symbolic aggregate approximation (faSAX) algorithm is proposed, which adaptively segments and assigns symbols to the segmented time-variant fatigue patterns according to the discrepancy in postural behavior and vehicle parameters. These multivariate symbols are then combined to prepar ....
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 ....