ArticleJournal of eye movement research2018
Synchronizing eye tracking and optical motion capture: How to bring them together.
Article in Journal of eye movement research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Application of Eye Tracking Technology in Aviation, Maritime, and Construction Industries: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- The fundamentals of eye tracking part 4: Tools for conducting an eye tracking study.Behavior research methods · 2025Article
- The application of eye-tracking in music research.Journal of eye movement research · 2019Article
- Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Both eye tracking and motion capture technologies are nowadays frequently used in human sciences, although both technologies are usually used separately. However, measuring both eye and body movements simultaneously would offer great potential for investigating crossmodal interaction in human (e.g. music and language-related) behavior. Here we combined an Ergoneers Dikablis head mounted eye tracker with a Qualisys Oqus optical motion capture system. In order to synchronize the recordings of both devices, we developed a generalizable solution that does not rely on any (cost-intensive) ready-made / company-provided synchronization solution. At the beginning of each recording, the participant nods quickly while fixing on a target while keeping the eyes open - a motion yielding a sharp vertical displacement in both mocap and eye data. This displacement can be reliably detected with a peak-picking algorithm and used for accurately aligning the mocap and eye data. This method produces accurate synchronization results in the case of clean data and therefore provides an attractive alternative to costly plug-ins, as well as a solution in case ready-made synchronization solutions are unavailable.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.