CLAS: A Database for Cognitive Load, Affect and Stress Recognition

We present the overall design and the implementation of the CLAS dataset, a multimodal resource which was purposely developed in support of research and technology development (RTD) activities oriented towards the automated recognition of some specific states of mind. Although the particular focus of our research is on the states of mind associated with negative emotions, mental strain and high cognitive effort, the CLAS dataset could offer an adequate support to research of a wider scope, such as general studies on attention assessment, cognitive load assessment, emotion recognition, as well as stress detection. The dataset consists of synchronized recordings of physiological signals, such as Electrocardiography (ECG), Plethysmography (PPG), ElectroDermal Activity (EDA), as well as accelerometer data, and metadata of 62 healthy volunteers, which were recorded while involved in three interactive tasks and two perceptive tasks. The interactive tasks aim to elicit different types of cognitive effort and included solving sequences of Math problems, Logic problems and the Stroop test. The perceptive tasks make use of images and audio-video stimuli, purposely selected to evoke emotions in the four quadrants of the arousal-valence space. The joint analysis of success rates in the interactive tasks and the information acquired through the questionnaire and the physiological recordings enables for a multifaceted evaluation of specific states of mind. These results are important for the advancement of research on efficient human-robot collaborations and general research on intelligent human-machine interaction interfaces.

本研究介绍了CLAS数据集的整体设计与实现方案。该数据集是为支持面向特定心理状态自动识别的研究与技术开发（RTD）活动而专门构建的多模态资源。尽管本研究的核心聚焦于与负性情绪、心理紧张及高认知负荷相关的心理状态，但CLAS数据集亦可对更广泛领域的研究提供充足支持，例如注意力评估、认知负荷评估、情绪识别以及压力检测等通用研究方向。该数据集包含62名健康志愿者的同步生理信号、加速度计数据及元数据，其中生理信号涵盖心电图（Electrocardiography，ECG）、容积描记法（Plethysmography，PPG）与皮肤电活动（ElectroDermal Activity，EDA），所有数据均采集于志愿者参与三项交互任务与两项感知任务的过程中。交互任务旨在诱发不同类型的认知负荷，具体包括求解数学题序列、逻辑题序列以及斯特鲁普测验（Stroop test）。感知任务则采用图像与音视频刺激材料，这些材料均经过专门挑选，用于唤起唤醒度-效价空间四个象限所对应的情绪。通过联合分析交互任务的成功率、问卷采集的信息以及生理信号记录数据，可对特定心理状态进行多维度评估。上述研究成果对于高效人机协作领域的研究进展，以及智能人机交互界面的通用研究均具有重要意义。