A Taxonomy and Systematic Review of Gaze Interactions for 2D Displays: Promising Techniques and Opportunities

IF 23.8 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

ACM Computing Surveys Pub Date : 2025-05-26 DOI:10.1145/3736250

Asma Shakil, Christof Lutteroth, Gerald Weber

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引用次数: 0

Abstract

Gaze input offers strong potential for creating intuitive and engaging user interfaces, but remains constrained by inherent limitations in accuracy and precision. Although extensive research has explored gaze-based interaction over the past three decades, a systematic framework that fully captures the diversity of gaze interaction techniques is still lacking. To address this gap, we present a novel two-dimensional taxonomy that classifies gaze interactions by (1) the type of input , distinguishing between gaze-only and gaze-assisted modalities, and (2) the type of target , differentiating between those requiring absolute gaze coordinates and thus higher accuracy, and those using relative coordinates, which tolerate lower accuracy. Our taxonomy explicitly captures the required input accuracy and interface constraints of each technique, providing clearer guidance for designers of gaze-based interfaces. We apply this taxonomy to review and classify 125 studies of active gaze interactions on 2D displays. The findings highlight promising techniques and identify research opportunities to advance gaze interaction design.

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二维显示器凝视交互的分类和系统综述：有前途的技术和机会

凝视输入为创造直观和吸引人的用户界面提供了强大的潜力，但仍然受到准确性和精度的固有限制。尽管在过去的三十年里，人们对基于凝视的交互进行了广泛的研究，但仍然缺乏一个系统的框架来充分捕捉凝视交互技术的多样性。为了解决这一差距，我们提出了一种新的二维分类法，通过(1)输入类型对凝视交互进行分类，区分只有凝视和辅助凝视的模式；(2)目标类型，区分需要绝对凝视坐标的模式和使用相对坐标的模式，前者需要更高的精度，后者需要更低的精度。我们的分类法明确地捕获了每种技术所需的输入准确性和接口约束，为基于注视的界面的设计人员提供了更清晰的指导。我们应用这种分类法来回顾和分类二维显示器上的125项主动凝视交互研究。这些发现突出了有前途的技术，并确定了推进凝视交互设计的研究机会。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACM Computing Surveys 工程技术-计算机：理论方法

CiteScore

33.20

自引率

0.60%

发文量

372

审稿时长

12 months

期刊介绍： ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.