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Updated: Jul 2, 2026
08:25
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
Published on: May 7, 2019
Analysis of human visual experience data
Johannes Zauner1,2,3,4, Aaron Nicholls5,6,7, Lisa A Ostrin8,9,10
1Technical University of Munich, TUM School of Medicine and Health, Department Health and Sport Sciences, Chronobiology & Health, Munich, Germany.
Journal of Vision
|July 1, 2026
Summary
This tutorial introduces an analysis pipeline for visual experience data, crucial for understanding human chronobiology and myopia. It standardizes processing of light exposure and viewing distance for reproducible research.
Area of Science:
- Ophthalmology
- Chronobiology
- Human Physiology
Background:
- Visual experience significantly impacts human physiology and behavior.
- Real-world visual environments are complex and dynamic, posing analysis challenges.
- Existing methods often fail to replicate real-world conditions.
Purpose of the Study:
- To present a standardized analysis pipeline for visual experience datasets.
- To focus on reproducible workflows for chronobiology and myopia research.
- To provide step-by-step instructions for data processing.
Main Methods:
- Importing, visualizing, and processing viewing distance and light exposure data.
- Performing time-series analyses for working distance and light metrics.
- Utilizing the open-source R package LightLogR for standardization.
Main Results:
- A modular analysis pipeline for visual experience data.
- Standardized methods for analyzing light exposure and viewing distance.
- Flexible workflows adaptable to diverse experimental setups.
Conclusions:
- The pipeline enables robust analysis of complex visual environment data.
- It supports researchers in chronobiology and myopia studies.
- Promotes flexible and reproducible research practices.
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