ArticleScientific reports2020
Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 117 citations in OpenAlex.
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- Decoding target discriminability and time pressure using eye and head movement features in a foraging search task.Cognitive research: principles and implications · 2025Article
- Approaches to understanding natural behavior.Journal of vision · 2025Review
- LEyes: A lightweight framework for deep learning-based eye tracking using synthetic eye images.Behavior research methods · 2025Article
- Cognitive load affects gaze dynamics during real-world tasks.Experimental brain research · 2025Article
- The fundamentals of eye tracking part 4: Tools for conducting an eye tracking study.Behavior research methods · 2025Article
- Point-SPV: end-to-end enhancement of object recognition in simulated prosthetic vision using synthetic viewing points.Frontiers in human neuroscience · 2025Article
- Protocol for assessing functional impairments in patients with unilateral and bilateral vestibulopathy: a novel approach to evaluate the impact of vestibular loss in daily life setting.Frontiers in neurology · 2025Article
- Vision toolkit part 1. Neurophysiological foundations and experimental paradigms in eye-tracking research: a review.Frontiers in physiology · 2025Review
- A dataset of paired head and eye movements during visual tasks in virtual environments.Scientific data · 2024Article
- Implementing mobile eye tracking in psychological research: A practical guide.Behavior research methods · 2024Review
- The visual experience dataset: Over 200 recorded hours of integrated eye movement, odometry, and egocentric video.Journal of vision · 2024Article
- A Child-Friendly Wearable Device for Quantifying Environmental Risk Factors for Myopia.Translational vision science & technology · 2024Article
- Large eye-head gaze shifts measured with a wearable eye tracker and an industrial camera.Behavior research methods · 2024Article
- Strategies for enhancing automatic fixation detection in head-mounted eye tracking.Behavior research methods · 2024Article
- ACE-DNV: Automatic classification of gaze events in dynamic natural viewing.Behavior research methods · 2024Article
- Scanning Laser Ophthalmoscopy Demonstrates Pediatric Optic Disc and Peripapillary Strain During Horizontal Eye Rotation.Current eye research · 2024Article
- High-fidelity eye, head, body, and world tracking with a wearable device.Behavior research methods · 2024Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study of gaze behavior has primarily been constrained to controlled environments in which the head is fixed. Consequently, little effort has been invested in the development of algorithms for the categorization of gaze events (e.g. fixations, pursuits, saccade, gaze shifts) while the head is free, and thus contributes to the velocity signals upon which classification algorithms typically operate. Our approach was to collect a novel, naturalistic, and multimodal dataset of eye + head movements when subjects performed everyday tasks while wearing a mobile eye tracker equipped with an inertial measurement unit and a 3D stereo camera. This Gaze-in-the-Wild dataset (GW) includes eye + head rotational velocities (deg/s), infrared eye images and scene imagery (RGB + D). A portion was labelled by coders into gaze motion events with a mutual agreement of 0.74 sample based Cohen's κ. This labelled data was used to train and evaluate two machine learning algorithms, Random Forest and a Recurrent Neural Network model, for gaze event classification. Assessment involved the application of established and novel event based performance metrics. Classifiers achieve ~87% human performance in detecting fixations and saccades but fall short (50%) on detecting pursuit movements. Moreover, pursuit classification is far worse in the absence of head movement information. A subsequent analysis of feature significance in our best performing model revealed that classification can be done using only the magnitudes of eye and head movements, potentially removing the need for calibration between the head and eye tracking systems. The GW dataset, trained classifiers and evaluation metrics will be made publicly available with the intention of facilitating growth in the emerging area of head-free gaze event classification.
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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.