Detection of Driving Fatigue Based on Grip Force on Steering Wheel with Wavelet Transformation and Support Vector Machine

  • Fan Li
  • Xiao-Wei Wang
  • Bao-Liang Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)


This paper proposes an unobtrusive way to detect fatigue for drivers through grip forces on steering wheel. Simulated driving experiments are conducted in a refitted passenger car, during which grip forces of both hands are collected. Wavelet transformation is introduced to extract fatigue-related features from wavelet coefficients. We compare the performance of k-nearest neighbours, linear discriminant analysis, and support vector machine (SVM) on the task of discriminating drowsy and awake states. SVM with radial basis function reaches the best accuracy, 75% on average. The results show that variation in grip forces on steering wheel can be used to effectively detect drivers’ fatigue.


fatigue detection grip force wavelet transformation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fan Li
    • 1
  • Xiao-Wei Wang
    • 1
  • Bao-Liang Lu
    • 1
    • 2
  1. 1.Center for Brain-like Computing and Machine Intelligence, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiP.R. China
  2. 2.MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent SystemsShanghai Jiao Tong UniversityShanghaiP.R. China

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