Detecting driver fatigue based on nonlinear speech processing and fuzzy SVM

The driving fatigue related human vocal organ changes can influence the strange atrractor's trajectories in phase space under a speech nonlinear dynamic model and correspondingly affect the chaos and fractal features of speech signal. Therefore, this paper proposes a method for detecting driver fatigue using nonlinear speech processing techniques combined with fuzzy support vector machine (SVM). First, we reconstructed the speech signal in phase space to build a speech nonlinear dynamic model, extracted and analyzed the changes of several nonlinear dynamic features such as the largest Lyapunov exponent, fractal dimension and approximate entropy when the degree of human fatigue changes. Then during the designing process of the multi-feature fatigue classifier, fuzzy clustering concept was introduced to the traditional SVM classifier, and the fuzzy membership degrees of the training speech samples are computed by a novel proposed membership function to overcome the sensitivity to noise and outliers. After training this fuzzy SVM, a multi-feature fusion classifier was established for fatigue recognition of drivers' speech samples. The experimental results confirm the feasibility and the effectiveness of this proposed method, and it seems to be promising in the application of driver fatigue detection.