Department Digital Health Systems, Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, 91058 Erlangen, Germany.
Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen Nuremberg, 91052 Erlangen, Germany.
Sensors (Basel). 2022 Dec 28;23(1):340. doi: 10.3390/s23010340.
Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as measurements such as action units extracted from facial videos, metrics like reaction time and feedback using questionnaires, we create (utonomous riving Cognitive Load ssessment Data) As a reference method to induce cognitive load onto subjects, we use the well-established -back test, in addition to our novel simulator-based -drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.
驾驶员监控系统在低级别到中级别自动驾驶汽车中起着重要作用。我们的工作重点是检测认知负荷作为驾驶员状态估计的一个组成部分，以提高交通安全。通过在 51 名受试者上诱导单一和双重任务工作负荷，同时连续测量来自多个模态的信号，基于测量，如心电图、EDA、EMG、PPG、呼吸率、皮肤温度和眼动追踪数据，以及从面部视频中提取的动作单元等测量，以及使用问卷的反应时间和反馈等指标，我们创建了（自主驾驶认知负荷评估数据）作为向受试者诱导认知负荷的参考方法，我们使用了成熟的 n-back 测试，以及我们基于新型模拟器的 -drive 测试，这是由现实世界中的半自动驾驶车辆驱动的。我们提取所有测量的专家特征，并发现多个模态中的显著变化。最终，我们使用单模态和多模态输入来训练和评估机器学习算法，以区分认知负荷水平。我们仔细评估模型行为并研究特征重要性。总之，我们引入了一种新的认知负荷测试，创建了一个认知负荷数据库，使用统计测试验证变化，引入了用于机器学习的新的分类和回归任务，并训练和评估了机器学习模型。
