论文标题
驱动器生理唤醒的变化点的多模式估计
Multimodal Estimation of Change Points of Physiological Arousal in Drivers
论文作者
论文摘要
检测不安全的驾驶状态，例如压力，嗜睡和疲劳，是确保驾驶安全性的重要组成部分，也是车辆自动干预系统的重要先决条件。这些有关条件主要连接到驾驶员的低或高唤醒水平。在这项研究中，我们描述了一个框架，用于在驾驶和定位驱动器生理唤醒状态显着变化的驾驶过程中处理多模式生理时间序列。这些变化点可能可能表明需要及时干预的事件。我们使用三个公共数据集将时间序列分割应用于心率和呼吸率测量值，并量化其在捕获电胚层活性变化点的鲁棒性，并使用三个公共数据集将其视为唤醒和自我报告的应力评级。我们的实验表明，生理措施是唤醒变化点的名副其实的指标，并在广泛的消融研究中表现出色。
Detecting unsafe driving states, such as stress, drowsiness, and fatigue, is an important component of ensuring driving safety and an essential prerequisite for automatic intervention systems in vehicles. These concerning conditions are primarily connected to the driver's low or high arousal levels. In this study, we describe a framework for processing multimodal physiological time-series from wearable sensors during driving and locating points of prominent change in drivers' physiological arousal state. These points of change could potentially indicate events that require just-in-time intervention. We apply time-series segmentation on heart rate and breathing rate measurements and quantify their robustness in capturing change points in electrodermal activity, treated as a reference index for arousal, as well as on self-reported stress ratings, using three public datasets. Our experiments demonstrate that physiological measures are veritable indicators of change points of arousal and perform robustly across an extensive ablation study.
