English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The interaction between the vestibular and ocular system has primarily beenstudied in controlled environments. Consequently, off-the shelf tools forcategorization of gaze events (e.g. fixations, pursuits, saccade) fail whenhead movements are allowed. Our approach was to collect a novel, naturalistic,and multimodal dataset of eye+head movements when subjects performed everydaytasks while wearing a mobile eye tracker equipped with an inertial measurementunit and a 3D stereo camera. This Gaze-in-the-Wild dataset (GW) includeseye+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 mutualagreement of 0.72 sample based Cohen's $\kappa$. This labelled data was used totrain and evaluate two machine learning algorithms, Random Forest and aRecurrent Neural Network model, for gaze event classification. Assessmentinvolved the application of established and novel event based performancemetrics. Classifiers achieve $\sim$90$\%$ human performance in detectingfixations and saccades but fall short (60$\%$) on detecting pursuit movements.Moreover, pursuit classification is far worse in the absence of head movementinformation. A subsequent analysis of feature significance in ourbest-performing model revealed a reliance upon absolute eye and head velocity,indicating that classification does not require spatial alignment of the headand eye tracking coordinate systems. The GW dataset, trained classifiers andevaluation metrics will be made publicly available with the intention offacilitating growth in the emerging area of head-free gaze eventclassification.

Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities