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
Driver-mental-fatigue
River-posture
Atigue-patterns
Atigue-posture
Pattern-recognition
Tax