Vergence Matching: Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection

Sidenmark, Ludwig; Clarke, Christopher; Newn, Joshua; Lystbæk, Mathias N.; Pfeuffer, Ken; Gellersen, Hans

doi:10.1145/3544548.3580685

Cited by 13 publications

(4 citation statements)

References 66 publications

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“…If the vergence movement corresponds to a specific target's depth alteration, that target is identified as the user's point of focus. Though this approach has proven effective in selecting small targets [106], it is often perceived as uncomfortable, difficult to execute consistently, and diverts the user's attention [56].…”

Section: Gaze-based Interactionsmentioning

confidence: 99%

Towards an Eye-Brain-Computer Interface: Combining Gaze with the Stimulus-Preceding Negativity for Target Selections in XR

Reddy,

Proulx,

Hirshfield

et al. 2024

Preprint

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Gaze-assisted interaction techniques enable intuitive selections without requiring manual pointing but can result in unintended selections, known as Midas touch. A confirmation trigger eliminates this issue but requires additional physical and conscious user effort. Brain-computer interfaces (BCIs), particularly passive BCIs harnessing anticipatory potentials such as the Stimulus-Preceding Negativity (SPN) - evoked when users anticipate a forthcoming stimulus - present an effortless implicit solution for selection confirmation. Within a VR context, our research uniquely demonstrates that SPN has the potential to decode intent towards the visually focused target. We reinforce the scientific understanding of its mechanism by addressing a confounding factor - we demonstrate that the SPN is driven by the user’s intent to select the target, not by the stimulus feedback itself. Furthermore, we examine the effect of familiarly placed targets, finding that SPN may be evoked quicker as users acclimatize to target locations; a key insight for everyday BCIs.CCS CONCEPTSHuman-centered computing→Virtual reality;Mixed / augmented reality;Accessibility technologies;Interaction techniques.ACM Reference FormatG. S. Rajshekar Reddy, Michael J. Proulx, Leanne Hirshfield, and Anthony J. Ries. 2024. Towards an Eye-Brain-Computer Interface: Combining Gaze with the Stimulus-Preceding Negativity for Target Selections in XR. InProceedings of the CHI Conference on Human Factors in Computing Systems (CHI ‘24), May 11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 17 pages.https://doi.org/10.1145/3613904.3641925

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Section: Gaze-based Interactionsmentioning

confidence: 99%

Towards an Eye-Brain-Computer Interface: Combining Gaze with the Stimulus-Preceding Negativity for Target Selections in XR

Reddy,

Proulx,

Hirshfield

et al. 2024

Preprint

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“…Previous works have shown the feasibility of visual depth estimations in the VR headsets by using binocular disparity and stereoscopic vergence in VR headset [10,40]. The concept of visual depth as a new interaction input has explored in the previous works, either by defining a semi-transparent window at a different focal depth [26,41,42], tracking voluntary vergence movements [3,13], or matching the vergence changes with the depth changes of a moving objects [34] in VR. However, all of these methods lack an end-to-end UI design to guide the users how to actively manipulate their visual depth.…”

Section: Gaze-based Vr Interactionmentioning

confidence: 99%

“…More recent works on gaze-based VR/AR interaction demonstrate the great potential of visual depth as an interaction input to solve the Midas touch problem. These methods either guide the user to look at physical or virtual objects at different depths [3,26,34,41,42] or rely on voluntary eye convergence and divergence [13,15] by asking the users focusing on the nose or imagining to fixate on some point behind the display plane. However, these works lack an intuitive and systematic User Interface (UI) design to guide the users in manipulating their visual depth, leading to limited application scenarios and potentially user frustration.…”

Section: Introductionmentioning

confidence: 99%

FocusFlow: 3D Gaze-Depth Interaction in Virtual Reality Leveraging Active Visual Depth Manipulation

Zhang,

Chen,

Shaffer

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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we propose two learning strategies that use different visual cues to help users master visual depth control. Our user studies on 24 participants demonstrate the usability of our proposed virtual window concept as a gaze-depth interaction method. In addition, our findings reveal that the user experience can be enhanced through an effective learning process with adaptive visual cues, helping users to develop muscle memory for this brand-new input mechanism. We conclude the paper by discussing strategies to optimize learning and potential research topics of gaze-depth interaction. CCS CONCEPTS• Human-centered computing → HCI design and evaluation methods; Virtual reality; Interaction design theory, concepts and paradigms.

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“…1,13,42], and selection and disambiguation techniques that do not rely on calibration [e.g. 26,32,33,45].…”

Section: Related Workmentioning

confidence: 99%

GazeSwitch: Automatic Eye-Head Mode Switching for Optimised Hands-Free Pointing

Hou,

Newn,

Sidenmark

et al. 2024

Proc. ACM Hum.-Comput. Interact.

Self Cite

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This paper contributes GazeSwitch, an ML-based technique that optimises the real-time switching between eye and head modes for fast and precise hands-free pointing. GazeSwitch reduces false positives from natural head movements and efficiently detects head gestures for input, resulting in an effective hands-free and adaptive technique for interaction. We conducted two user studies to evaluate its performance and user experience. Comparative analyses with baseline switching techniques, Eye+Head Pinpointing (manual) and BimodalGaze (threshold-based) revealed several trade-offs. We found that GazeSwitch provides a natural and effortless experience but trades off control and stability compared to manual mode switching, and requires less head movement compared to BimodalGaze. This work demonstrates the effectiveness of machine learning approach to learn and adapt to patterns in head movement, allowing us to better leverage the synergistic relation between eye and head input modalities for interaction in mixed and extended reality.

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Vergence Matching: Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection

Cited by 13 publications

References 66 publications

Towards an Eye-Brain-Computer Interface: Combining Gaze with the Stimulus-Preceding Negativity for Target Selections in XR

Towards an Eye-Brain-Computer Interface: Combining Gaze with the Stimulus-Preceding Negativity for Target Selections in XR

FocusFlow: 3D Gaze-Depth Interaction in Virtual Reality Leveraging Active Visual Depth Manipulation

GazeSwitch: Automatic Eye-Head Mode Switching for Optimised Hands-Free Pointing

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