Abstract
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.
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Li, F., Wang, XW., Lu, BL. (2013). Detection of Driving Fatigue Based on Grip Force on Steering Wheel with Wavelet Transformation and Support Vector Machine. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_19
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DOI: https://doi.org/10.1007/978-3-642-42051-1_19
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