Empirical Evaluation of Moving Target Selection in Virtual Reality Using Egocentric Metaphors - Human-Computer Interaction – INTERACT 2021
Conference Papers Year : 2021

Empirical Evaluation of Moving Target Selection in Virtual Reality Using Egocentric Metaphors

Junwei Sun
  • Function : Author
  • PersonId : 1286564
Qiang Xu
  • Function : Author
  • PersonId : 1286565
Edward Lank
  • Function : Author
  • PersonId : 1234577
Pourang Irani
  • Function : Author
  • PersonId : 936715
Wei Li
  • Function : Author
  • PersonId : 1286566

Abstract

Virtual hand or pointer metaphors are among the key approaches for target selection in immersive environments. However, targeting moving objects is complicated by factors including target speed, direction, and depth, such that a basic implementation of these techniques might fail to optimize user performance. We present results of two empirical studies comparing characteristics of virtual hand and pointer metaphors for moving target acquisition. Through a first study, we examine the impact of depth on users’ performance when targets move beyond and within arms’ reach. We find that movement in depth has a great impact on both metaphors. In a follow-up study, we design a reach-bounded Go-Go (rbGo-Go) technique to address challenges of virtual hand and compare it to Ray-Casting. We find that target width and speed are significant determinants of user performance and we highlight the pros and cons for each of the techniques in the given context. Our results inform the UI design for immersive selection of moving targets.
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hal-04215513 , version 1 (22-09-2023)

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Yuan Chen, Junwei Sun, Qiang Xu, Edward Lank, Pourang Irani, et al.. Empirical Evaluation of Moving Target Selection in Virtual Reality Using Egocentric Metaphors. 18th IFIP Conference on Human-Computer Interaction (INTERACT), Aug 2021, Bari, Italy. pp.29-50, ⟨10.1007/978-3-030-85610-6_3⟩. ⟨hal-04215513⟩
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