GAZELOAD: 産業用ヒューマンロボット協調作業におけるメンタルワークロード計測のためのマルチモーダル視線追跡データセット
GAZELOAD A Multimodal Eye-Tracking Dataset for Mental Workload in Industrial Human-Robot Collaboration
産業用協働ロボットとの作業中にスマートグラスで計測した視線・瞳孔データと環境・タスク情報を同期したデータセットを構築し、作業負荷推定アルゴリズムの評価基盤を提供する。
著者: Bsher Karbouj, Baha Eddin Gaaloul, Jorg Kruger
分類: cs.RO
原文アブストラクト
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.