Detection of Driving Fatigue Based on Grip Force on Steering Wheel with Wavelet Transformation and Support Vector Machine
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.
Keywordsfatigue detection grip force wavelet transformation
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