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

2026-01-29

AI Summary

This study addresses the lack of multimodal, temporally synchronized eye-tracking datasets for cognitive load assessment in industrial human-robot collaboration scenarios. In a controlled experiment, 26 participants were recruited to perform tasks while wearing Meta ARIA smart glasses, which captured high-temporal-resolution eye-tracking signals—including pupil diameter, fixations, and saccades—alongside ambient illuminance and task logs. All data streams were aligned in 250-ms windows, and subjective cognitive load ratings together with task difficulty labels were recorded. This open-source dataset represents the first fine-grained synchronization of eye-tracking, illumination, and task context in an industrial collaborative setting, revealing the influence of environmental factors on oculomotor metrics and providing a standardized benchmark for cognitive load estimation, feature extraction, and temporal modeling.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Motion & Tracking
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• mental workload
• human-robot collaboration
• eye-tracking
• industrial environment
• psychophysiological measurement

Innovation

• multimodal eye-tracking
• mental workload estimation
• human-robot collaboration
• temporal synchronization
• environmental context