Driver Fatigue Detection Based on Multiple Physiological Signals and an Improved Deep Belief Network

Abstract

In order to accurately discriminate the driver fatigue, multiple physiological signals of 10 drivers were collected by a wireless body area network in actual driving, including neck electromyography (EMG) and electroencephalography (EEG). Then, the noises of signals were removed by several denoising methods, and 22 features were extracted, including energy entropy, multiscale entropy, and other relevant features. Subsequently, a deep belief network (DBN) was used to further extract multi-domain features. And then, a grey wolf optimization algorithm was used to optimize the performance of the DBN. The results showed that the accuracy of the model built in the present work was up to 96% in discriminating the fatigue states.