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Subspace-based feature extraction on multi-physiological measurements of automobile drivers for distress recognition

机译：基于子空间的特征提取对汽车驱动因素的多生理测量识别

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摘要

The automotive industry has accelerated the utilization of Intelligent Transport Systems (ITS) in vehicles for increased driving safety. In this paper, a novel and well-done subspace feature extraction scheme on the physiological signals acquired by wearable sensors, for drivers' distress level detection to be introduced as an ITS is proposed and verified on the publicly available MIT-BIH PhysioNet Multi-parameter Database. The proposed scheme includes two phases where time-domain statistical feature extraction is first realized on the electrocardiogram (ECG), hand galvanic skin response (hand GSR), foot galvanic skin response (foot GSR), electromyogram (EMG), and respiration (RESP) signals, and secondly subspace feature vector construction is appreciated by applying Discriminative Common Vector (DCV) decomposition on the statistical feature vectors. The distress levels of the drivers are determined as low, moderate, and high by utilizing both the statistical and the subspace feature vectors using Support Vector Machines (SVM) classifier by 2-fold cross-validation technique. A maximum of 88.89 % classification accuracy is achieved using statistical features in 7384 s while it is increased to 100 % in 3,421 s when subspace features are employed. The increased classification accuracy in decreased time consumption evidently shows the success of the proposed feature extraction scheme.
机译：汽车行业加快了利用智能运输系统（其）在车辆中增加了驾驶安全性。在本文中，提出了一种新颖的和完成的子空间特征提取方案，用于通过可穿戴传感器获取的生理信号，用于推出作为其它的驱动器的遇险电平检测，并在公开的MIT-BIH PhysioIonet Multi参数上验证数据库。所提出的方案包括在心电图（ECG）上首先实现时域统计特征提取的两个阶段，手动电流皮肤响应（手GSR），脚电流皮肤响应（脚GSR），电灰度（EMG）和呼吸（RESP通过在统计特征向量上应用鉴别的常见常见载体（DCV）分解来理解，通过应用鉴别的常见常见矢量（DCV）分解来理解信号和第二子空间特征向量。通过使用支撑向量机（SVM）分类器的统计和子空间特征向量通过2倍交叉验证技术，驾驶员的遇险级别确定为低，中等和高。使用7384年的统计特征实现了最多88.89％的分类准确度，而使用子空间功能时，在3,421秒内增加到100％。减少时间消耗中的分类精度增加明显显示了所提出的特征提取方案的成功。

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