使用多尺度一维卷积神经网络检测缓慢眼动，用于驾驶员瞌睡检测。

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

机构信息

Center for Brain-like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai 200240, China.

出版信息

J Neurosci Methods. 2023 Sep 1;397:109939. doi: 10.1016/j.jneumeth.2023.109939. Epub 2023 Aug 12.

DOI:10.1016/j.jneumeth.2023.109939

PMID:37579794

Abstract

BACKGROUND

Slow eye movements (SEMs), which occurs during eye-closed periods with high time coverage rate during simulated driving process, indicate drivers' sleep onset.

NEW METHOD

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.

RESULTS

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.

COMPARISON WITH EXISTING METHOD

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.

CONCLUSIONS

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.

摘要

背景

在模拟驾驶过程中，闭眼期间的眼球运动（SEMs）具有高时间覆盖率，表明驾驶员进入睡眠状态。

新方法

针对慢眼动波形的多尺度特征，我们提出了一种多尺度一维卷积神经网络（MS-1D-CNN）进行分类。MS-1D-CNN 对原始信号进行多次下采样处理分支，并使用局部卷积层提取每个分支的特征。

结果

我们通过受试者间评估和一次剔除一位受试者的交叉验证，分别在十位受试者的标准训练-测试数据集和连续测试数据集上评估了该模型的分类性能。对于标准训练-测试数据集，受试者间评估和一次剔除一位受试者的交叉验证的总体平均分类准确率分别约为 99.1%和 98.6%。对于连续测试数据集，受试者间评估的准确率、精确率、召回率和 F1 分数的总体平均值分别为 99.3%、98.9%、99.5%和 99.1%，一次剔除一位受试者的交叉验证的准确率、精确率、召回率和 F1 分数的总体平均值分别为 99.2%、98.8%、99.3%和 99.0%。