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首页> 美国卫生研究院文献>Frontiers in Human Neuroscience >A Novel Method for Classifying Driver Mental Workload Under Naturalistic Conditions With Information From Near-Infrared Spectroscopy
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A Novel Method for Classifying Driver Mental Workload Under Naturalistic Conditions With Information From Near-Infrared Spectroscopy

机译：利用近红外光谱信息对自然条件下驾驶员心理工作量进行分类的新方法

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Driver cognitive distraction is a critical factor in road safety, and its evaluation, especially under real conditions, presents challenges to researchers and engineers. In this study, we considered mental workload from a secondary task as a potential source of cognitive distraction and aimed to estimate the increased cognitive load on the driver with a four-channel near-infrared spectroscopy (NIRS) device by introducing a machine-learning method for hemodynamic data. To produce added cognitive workload in a driver beyond just driving, two levels of an auditory presentation n-back task were used. A total of 60 experimental data sets from the NIRS device during two driving tasks were obtained and analyzed by machine-learning algorithms. We used two techniques to prevent overfitting of the classification models: (1) k-fold cross-validation and principal-component analysis, and (2) retaining 25% of the data (testing data) for testing of the model after classification. Six types of classifier were trained and tested: decision tree, discriminant analysis, logistic regression, the support vector machine, the nearest neighbor classifier, and the ensemble classifier. Cognitive workload levels were well classified from the NIRS data in the cases of subject-dependent classification (the accuracy of classification increased from 81.30 to 95.40%, and the accuracy of prediction of the testing data was 82.18 to 96.08%), subject 26 independent classification (the accuracy of classification increased from 84.90 to 89.50%, and the accuracy of prediction of the testing data increased from 84.08 to 89.91%), and channel-independent classification (classification 82.90%, prediction 82.74%). NIRS data in conjunction with an artificial intelligence method can therefore be used to classify mental workload as a source of potential cognitive distraction in real time under naturalistic conditions; this information may be utilized in driver assistance systems to prevent road accidents.
机译：驾驶员的认知分散是道路安全的关键因素，尤其是在实际条件下，其评估对研究人员和工程师提出了挑战。在这项研究中，我们将次要任务中的精神工作量视为潜在的认知干扰源，旨在通过引入机器学习方法来估计四通道近红外光谱（NIRS）设备对驾驶员的认知负荷增加用于血液动力学数据。为了在驾驶者之外产生更多的认知工作量，使用了两个级别的听觉提示n向后任务。在两个驾驶任务期间，从NIRS设备获得了总共60个实验数据集，并通过机器学习算法进行了分析。我们使用了两种技术来防止分类模型的过度拟合：（1）k倍交叉验证和主成分分析，以及（2）保留25％的数据（测试数据）用于分类后的模型测试。训练并测试了六种类型的分类器：决策树，判别分析，逻辑回归，支持向量机，最近邻分类器和集合分类器。在主题依赖分类的情况下，根据NIRS数据对认知工作量水平进行了很好的分类（分类准确度从81.30提高到95.40％，测试数据预测的准确度从82.18提高到96.08％），主题26独立分类（分类的准确度从84.90增加到89.50％，测试数据的预测准确度从84.08上升到89.91％）和独立于通道的分类（分类82.90％，预测82.74％）。因此，在自然条件下，NIRS数据与人工智能方法相结合可用于将心理工作量归为实时潜在的潜在注意力分散来源。该信息可用于驾驶员辅助系统中，以防止发生交通事故。

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