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Unsupervised Patterns of Driver Mental Fatigue State Based on Head Posture Using Gaussian Mixture Model

Conference Paper


Abstract


  • Monitoring and interpreting of the Driver behaviour is a challenging area of research as the driver behaves unpredictably under the influence of factors such as fatigue, drowsiness, inattention, weather, traffic, and roads variations. In the studies reported in the literature, the activities of the driver are monitored through longitudinal and lateral behaviours using physiological characteristics and computer vision. However, an effective method to understand and monitor the sudden changes in the postural behaviour of the driver leading to catastrophic conditions is still outstanding. Towards developing such method, the driver head posture under different driving states is monitored in this study using inertial sensors. The data produced by the sensors is modelled using Gaussian Mixture unsupervised clustering approach. The experiments were conducted on a total of 10 young healthy subjects on MATHWORKS driver-in-loop simulator, interfaced with a virtual environment designed in Unreal Engine studio. A criteria of minimum abundance range between 0.5-1% is deployed to identify the most optimum clusters. The information contained in the clusters are analyzed to find the maximum magnitude and standard deviation of each cluster and then organized in descending order for assignment the symbols. Finally, the patterns of each driver state are validated. The results indicate that the proposed approach is effective in identifying the driver state in an unsupervised manner. Moreover, the patterns identified can be deployed in a smart early intervention system to correct the mistakes made during driving. The limitations of the current work and directions for future work are discussed.

Publication Date


  • 2020

Citation


  • Ansari, S., Du, H., Naghdy, F., & Stirling, D. (2020). Unsupervised Patterns of Driver Mental Fatigue State Based on Head Posture Using Gaussian Mixture Model. In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 2699-2704). doi:10.1109/SSCI47803.2020.9308534

Scopus Eid


  • 2-s2.0-85099719186

Web Of Science Accession Number


Start Page


  • 2699

End Page


  • 2704

Abstract


  • Monitoring and interpreting of the Driver behaviour is a challenging area of research as the driver behaves unpredictably under the influence of factors such as fatigue, drowsiness, inattention, weather, traffic, and roads variations. In the studies reported in the literature, the activities of the driver are monitored through longitudinal and lateral behaviours using physiological characteristics and computer vision. However, an effective method to understand and monitor the sudden changes in the postural behaviour of the driver leading to catastrophic conditions is still outstanding. Towards developing such method, the driver head posture under different driving states is monitored in this study using inertial sensors. The data produced by the sensors is modelled using Gaussian Mixture unsupervised clustering approach. The experiments were conducted on a total of 10 young healthy subjects on MATHWORKS driver-in-loop simulator, interfaced with a virtual environment designed in Unreal Engine studio. A criteria of minimum abundance range between 0.5-1% is deployed to identify the most optimum clusters. The information contained in the clusters are analyzed to find the maximum magnitude and standard deviation of each cluster and then organized in descending order for assignment the symbols. Finally, the patterns of each driver state are validated. The results indicate that the proposed approach is effective in identifying the driver state in an unsupervised manner. Moreover, the patterns identified can be deployed in a smart early intervention system to correct the mistakes made during driving. The limitations of the current work and directions for future work are discussed.

Publication Date


  • 2020

Citation


  • Ansari, S., Du, H., Naghdy, F., & Stirling, D. (2020). Unsupervised Patterns of Driver Mental Fatigue State Based on Head Posture Using Gaussian Mixture Model. In 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (pp. 2699-2704). doi:10.1109/SSCI47803.2020.9308534

Scopus Eid


  • 2-s2.0-85099719186

Web Of Science Accession Number


Start Page


  • 2699

End Page


  • 2704