GAZELOAD A Multimodal Eye-Tracking Dataset for Mental Workload in Industrial Human-Robot Collaboration

TL;DR

GAZELOAD is a comprehensive multimodal dataset capturing eye-tracking and environmental data during human-robot collaboration tasks, enabling advanced mental workload estimation research in industrial settings.

Contribution

The paper introduces GAZELOAD, a novel dataset combining eye-tracking, environmental, and task data for mental workload analysis in industrial HRC scenarios.

Findings

Dataset includes synchronized eye-tracking and environmental data.

Provides mental workload ratings and task context annotations.

Facilitates development of workload estimation algorithms.

Abstract

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.