Detecting slow eye movements using multi-scale one-dimensional convolutional neural network for driver sleepiness detection

Slow eye movements (SEMs), which occurs during eye-closed periods with high time coverage rate during simulated driving process, indicate drivers’ sleep onset. For the multi-scale characteristics of slow eye movement waveforms, we propose a multi-scale one-dimensional convolutional neural network (MS-1D-CNN) for classification. The MS-1D-CNN performs multiple down-sampling processing branches on the original signal and uses the local convolutional layer to extract the features for each branch. We evaluate the classification performance of this model on ten subjects’ standard train–test datasets and continuous test datasets by means of subject-subject evaluation and leave-one-subject-out cross validation, respectively. For the standard train–test datasets, the overall average classification accuracies are about 99.1% and 98.6%, in subject-subject evaluation and leave-one-subject-out cross validation, respectively. For the continuous test datasets, the overall average values of accuracy, precision, recall and F1-score are 99.3%, 98.9%, 99.5% and 99.1% in subject-subject evaluation, are 99.2%, 98.8%, 99.3% and 99.0% in leave-one-subject-out cross validation. Results of the standard train–test datasets show that the overall average classification accuracy of the MS-1D-CNN is quite higher than the baseline method based on hand-designed features by 3.5% and 3.5%, in subject-subject evaluation and leave-one-subject-out cross validation, respectively. These results suggest that multi-scale transformation in the MS-1D-CNN model can enhance the representation ability of features, thereby improving classification accuracy. Experimental results verify the good performance of the MS-1D-CNN model, even in leave-one-subject-out cross validation, thus promoting the application of SEMs detection technology for driver sleepiness detection.