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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 36/2023

08.06.2023 | S.I. : Evolutionary Computation based Methods and Applications for Data Processing

Modeling the gaze point distribution to assist eye-based target selection in head-mounted displays

verfasst von: Ting Lei, Jing Chen, Jixiang Chen, Bo Liu

Erschienen in: Neural Computing and Applications | Ausgabe 36/2023

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Abstract

Due to its natural and fast characteristics, eye tracking, as a novel input modality, has been widely used in head-mounted displays for interaction. However, because of the inadvertent jitter of eyes and limitations of eye tracking devices, the eye-based selection often performs poorly in accuracy and stability compared with other input modalities, especially for small targets. To address this issue, we built a likelihood model by modeling the gaze point distribution and then combined it with Bayesian rules to infer the intended target from the perspective of probability as an alternative to the traditional selection criteria based on boundary judgment. Our investigation shows that using our model improves the selection performance significantly over the conventional ray-casting selection method and using the existing optimal likelihood model, especially in the selection of small targets.
Vorheriger Artikel Dynamic response prediction of underwater explosive vessel based on LOO-XGBoost model
Nächster Artikel Exploration of a network security situational awareness model based on multisource data fusion

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Metadaten
Titel
Modeling the gaze point distribution to assist eye-based target selection in head-mounted displays
verfasst von
Ting Lei
Jing Chen
Jixiang Chen
Bo Liu
Publikationsdatum
08.06.2023
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 36/2023
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-023-08705-8

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