构建工业人机协作中用于心理负荷评估的多模态眼动数据集。
GAZELOAD A Multimodal Eye-Tracking Dataset for Mental Workload in Industrial Human-Robot Collaboration
- 在实验室装配环境中同步采集眼动、环境与任务上下文数据。
- 26名参与者完成10种任务，提供每250毫秒的眼动指标和主观评分。
- 适合开发眼动负荷预测算法或研究光照等环境因素影响。
本文介绍GAZELOAD，一个面向工业人机协作中心理负荷估计的多模态数据集。数据在实验室装配平台采集，26名参与者佩戴Meta ARIA智能眼镜，与两台协作机器人（UR5和Franka Emika Panda）交互。数据同步记录眼动信号（瞳孔直径、注视点、眼跳、注视方向、注视转移熵、注视分散指数）、环境实时连续测量（照度）以及任务与机器人上下文（工作台、任务区块、人为故障），并在受控条件下调节任务难度与环境条件。每个参与者和负荷分级的任务区块均提供以250毫秒为窗口聚合的眼动指标CSV文件、环境日志及1-10级李克特量表的自评心理负荷评分，按参与者分目录组织并附带文档。该数据可用于开发与基准测试心理负荷估计、特征提取与时间建模算法，并探究光照等环境因素对基于眼动的负荷标志物的影响。
原文摘要 · Abstract (English)
This article describes GAZELOAD, a multimodal dataset for mental workload estimation in industrial human-robot collaboration. The data were collected in a laboratory assembly testbed where 26 participants interacted with two collaborative robots (UR5 and Franka Emika Panda) while wearing Meta ARIA smart glasses. The dataset time-synchronizes eye-tracking signals (pupil diameter, fixations, saccades, eye gaze, gaze transition entropy, fixation dispersion index) with environmental real-time and continuous measurements (illuminance) and task and robot context (bench, task block, induced faults), under controlled manipulations of task difficulty and ambient conditions. For each participant and workload-graded task block, we provide CSV files with ocular metrics aggregated into 250 ms windows, environmental logs, and self-reported mental workload ratings on a 1-10 Likert scale, organized in participant-specific folders alongside documentation. These data can be used to develop and benchmark algorithms for mental workload estimation, feature extraction, and temporal modeling in realistic industrial HRC scenarios, and to investigate the influence of environmental factors such as lighting on eye-based workload markers.
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