Heavy Truck Driver's Drowsiness Detection Method Using Wearable EEG Based on Convolution Neural Network

Heavy truck drivers driving trucks under the situation of drowsiness can cause serious traffic accidents. In this paper, a heavy truck driver's drowsiness detection method using wearable electroencephalographic (EEG) based on convolution neural network (CNN) is proposed. The presented method consists of three parts: data collection using wearable EEG, heavy truck driver's drowsiness detection and the early warning strategy. Firstly, a homemade wearable brain computer interface (BCI) is used to monitor and collect the EEG signals in the simulation environment of drowsiness driving. Secondly, the neural networks with Inception module and improved AlexNet module are trained to classify the EEG signals. Finally, the early warning strategy module will function and it will sound an alarm if the heavy truck driver is judged as drowsy. The method was tested on driving EEG data from simulated drowsiness driving. The results show that using neural network with Inception module reached 95.59% correct classification in a one second time window and using improved AlexNet module reached 94.68%. The simulation and test results demonstrate the feasibility of the proposed drowsiness detection method for heavy truck driving safety.