IEEE Trans Biomed Eng. 2020 Dec;67(12):3413-3424. doi: 10.1109/TBME.2020.2987168. Epub 2020 Nov 19.
in this paper we propose a system to detect a subject's sympathetic reaction, which is related to unexpected or challenging events during a car drive.
we use the Electrocardiogram (ECG) signal and the Skin Potential Response (SPR) signal, which has several advantages with respect to other Electrodermal (EDA) signals. We record one SPR signal for each hand, and use an algorithm that, selecting the smoother signal, is able to remove motion artifacts. We extract statistical features from the ECG and SPR signals in order to classify signal segments and identify the presence or absence of emotional events via a Supervised Learning Algorithm. The experiments were carried out in a company which specializes in driving simulator equipment, using a motorized platform and a driving simulator. Different subjects were tested with this setup, with different challenging events happening on predetermined locations on the track.
we obtain an Accuracy as high as 79.10% for signal blocks and as high as 91.27% for events.
results demonstrate the good performance of the presented system in detecting sympathetic reactions, and the effectiveness of the motion artifact removal procedure.
our work demonstrates the possibility to classify the emotional state of the driver, using the ECG and EDA signals and a slightly invasive setup. In particular, the proposed use of SPR and of the motion artifact removal procedure are crucial for the effectiveness of the system.
本文提出了一种检测受试者交感反应的系统，该反应与驾驶过程中意外或具有挑战性的事件有关。
我们使用心电图（ECG）信号和皮肤电位反应（SPR）信号，相对于其他皮肤电（EDA）信号，这些信号具有几个优点。我们为每只手记录一个 SPR 信号，并使用一种算法，该算法通过选择更平滑的信号，能够去除运动伪影。我们从 ECG 和 SPR 信号中提取统计特征，以便通过监督学习算法对信号段进行分类，并识别是否存在情感事件。实验在一家专门从事驾驶模拟器设备的公司进行，使用机动平台和驾驶模拟器。使用这种设置对不同的受试者进行了测试，在轨道的预定位置会发生不同的挑战性事件。
我们得到了高达 79.10%的信号块准确率和高达 91.27%的事件准确率。
结果表明，该系统在检测交感反应方面具有良好的性能，并且运动伪影去除过程有效。
我们的工作证明了使用 ECG 和 EDA 信号以及稍具侵入性的设置来对驾驶员的情绪状态进行分类的可能性。特别是，所提出的 SPR 的使用和运动伪影去除过程对于系统的有效性至关重要。
