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{{see also|Terms|Technical Terms}}
{{see also|Terms|Technical Terms}}
[[Eye tracking]] is a sensor technology that measures [[eye]] positions and [[eye movement]]. In the context of [[virtual reality]] (VR) and [[augmented reality]] (AR), it refers to the integration of sensors within [[VR headset]]s or [[AR headset]]s to determine precisely where the user is looking ([[gaze]]) in real-time within the [[virtual environment]] or overlaid digital interface. By accurately monitoring characteristics like pupil position, corneal reflections, and eye movements such as [[saccades]] and fixations, this technology enables more immersive, efficient, and intuitive user experiences. It has become an increasingly critical feature in modern [[head-mounted display]]s ([[HMD]]s), driving advancements in rendering, interaction, analytics, and social presence.<ref name="Duchowski2017">Duchowski, A. T. (2017). *Eye Tracking Methodology: Theory and Practice*. Springer.</ref>
[[Eye tracking]] is a sensor technology that measures [[eye]] positions and [[eye movement]]. In the context of [[virtual reality]] (VR) and [[augmented reality]] (AR), it refers to the integration of sensors within [[VR headset]]s or [[AR headset]]s to determine precisely where the user is looking ([[gaze]]) in real time inside a [[virtual environment]] or across an overlaid digital interface. By accurately monitoring characteristics such as pupil position, corneal reflections, and eye movements (for example [[saccades]] and fixations), the technology enables more immersive, efficient, and intuitive user experiences. Eye tracking has become a critical feature of modern [[head-mounted display]]s ([[HMD]]s), driving advances in rendering, interaction, analytics, and social presence.<ref name="Duchowski2017">Duchowski, A. T. (2017). ''Eye Tracking Methodology: Theory and Practice''. Springer.</ref>


==History==
==History==
While its integration into consumer VR/AR is relatively recent, the systematic study of eye movements dates back to the 19th century. [[Louis Émile Javal]] noted in 1879 that reading involved discrete stops (fixations) and rapid movements (saccades).<ref name="Rayner1998">Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. *Psychological Bulletin, 124*(3), 372–422.</ref> Early tracking devices included [[Edmund Huey]]'s contact lens-based tracker (~1908)<ref name="Huey1908">Huey, E.B. (1908). *The Psychology and Pedagogy of Reading*. Macmillan.</ref> and [[Guy Thomas Buswell]]'s film-based systems in the 1930s. [[Alfred L. Yarbus]]'s work in the 1960s highlighted how viewing patterns are task-dependent.<ref name="Yarbus1967">Yarbus, A.L. (1967). *Eye Movements and Vision*. Plenum Press.</ref> These foundational efforts paved the way for modern video-based and integrated tracking systems.
Systematic study of eye movements began in the 19th century. [[Louis Émile Javal]] observed in 1879 that reading involves discrete fixations and rapid saccades.<ref name="Rayner1998">Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. ''Psychological Bulletin, 124''(3), 372–422.</ref> Early instruments included [[Edmund Huey]]’s contact-lens tracker (≈1908),<ref name="Huey1908">Huey, E. B. (1908). ''The Psychology and Pedagogy of Reading''. Macmillan.</ref> [[Guy T. Buswell]]’s film trackers in the 1930s, and [[Alfred L. Yarbus]]’s seminal work on task-dependent gaze in the 1960s.<ref name="Yarbus1967">Yarbus, A. L. (1967). ''Eye Movements and Vision''. Plenum Press.</ref> Video-based systems emerged in the 1970s, while compact infrared trackers suitable for HMDs appeared in the 2010s.


==Technical Principles==
==Technical Principles==
===Tracking Method===
===Tracking methods===
Most eye tracking systems in contemporary VR and AR headsets employ one of several core technologies:
Most contemporary HMDs use one of four foundations:


*'''[[Pupil Center Corneal Reflection]] (PCCR)''': This is the predominant method. It involves:<ref name="Holmqvist2011">Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Weijer, J. (2011). *Eye tracking: A comprehensive guide to methods and measures*. Oxford University Press.</ref><ref name="RoadToVREyeTracking">Lang, B. (2021, July 21). *Casual Explainer: What is Eye-tracking & How Does it Work?* Road to VR. https://www.roadtovr.com/casual-explainer-what-is-eye-tracking-how-does-it-work/</ref>
*'''[[Pupil-centre/corneal-reflection]] (PCCR)''': Infrared (IR) LEDs illuminate the eye; IR cameras capture images; computer-vision algorithms locate the pupil centre and one or more corneal “glints”; the vector between them yields 3-D gaze.<ref name="Holmqvist2011">Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., &amp; Van de Weijer, J. (2011). ''Eye Tracking: A Comprehensive Guide to Methods and Measures''. Oxford University Press.</ref><ref name="RoadToVREyeTracking">Lang, B. (2023, May 2). ''Eye-tracking Is a Game-Changer for XR That Goes Far Beyond Foveated Rendering''. Road to VR. https://www.roadtovr.com/why-eye-tracking-is-a-game-changer-for-vr-headsets-virtual-reality/</ref>
*'''Illumination:''' [[Infrared]] (IR) light-emitting diodes ([[LED]]s) safely illuminate the eye. IR light is used because it is invisible to the human eye, preventing distraction, and provides high contrast for [[camera]]s.
*'''Imaging:''' Small, high-[[frame rate]] infrared cameras capture images of the eye, specifically tracking the center of the [[pupil]] and the reflection(s) of the IR light off the surface of the [[cornea]] (known as glints).
*'''[[Algorithm|Algorithmic Processing]]:''' Sophisticated [[computer vision]] and [[image processing]] algorithms analyze the captured images. By calculating the vector between the pupil center and the corneal reflection(s), the system determines the eye's orientation and calculates the user's gaze point with high accuracy.
*'''[[Calibration]]:''' A per-user calibration process is usually required upon first use, and sometimes periodically, to account for individual differences in eye physiology (for example corneal shape, pupil size range) and the precise fit of the headset. This typically involves the user looking at specific points displayed within the headset.


*'''Video-based eye tracking (Shape/Feature Tracking)''': Uses cameras aimed at the eyes to capture images, which are then analyzed using computer vision algorithms to identify eye features (pupil outline, iris texture, blood vessels) to determine gaze direction without necessarily relying on corneal reflections.<ref name="Hansen2010">Hansen, D. W., & Ji, Q. (2010). In the eye of the beholder: A survey of models for eyes and gaze. *IEEE Transactions on Pattern Analysis and Machine Intelligence, 32*(3), 478-500.</ref> PCCR is often considered a subset of this broader category.
*'''Video-feature tracking''': Cameras analyse iris texture, pupil outline, or eye-surface vessels; PCCR can be treated as a subset.<ref name="Hansen2010">Hansen, D. W., &amp; Ji, Q. (2010). In the eye of the beholder: A survey of models for eyes and gaze. ''IEEE Transactions on Pattern Analysis and Machine Intelligence, 32''(3), 478–500.</ref>


*'''[[Electrooculography]] (EOG)''': Measures the electrical potential difference between electrodes placed on the skin around the eyes. This potential changes predictably as the eye rotates. While less common for high-accuracy gaze pointing in consumer VR/AR due to lower precision and susceptibility to muscle noise, it can be used for detecting larger eye movements or in specialized applications.<ref name="Bulling2011">Bulling, A., Ward, J. A., Gellersen, H., & Tröster, G. (2011). Eye movement analysis for activity recognition using electrooculography. *IEEE Transactions on Pattern Analysis and Machine Intelligence, 33*(4), 741-753.</ref>
*'''[[Electrooculography]] (EOG)''': Electrodes around the eye sense corneo-retinal potential changes; useful for coarse movements or eyelid activity when cameras are unsuitable.<ref name="Bulling2011">Bulling, A., Ward, J. A., Gellersen, H., &amp; Tröster, G. (2011). Eye movement analysis for activity recognition using electrooculography. ''IEEE Transactions on Pattern Analysis and Machine Intelligence, 33''(4), 741–753.</ref>


*'''[[Scleral search coil]]''': Involves wearing a special contact lens containing a wire coil. The user sits within a magnetic field, and eye movements induce currents in the coil, providing extremely precise measurements. This is highly invasive and primarily used in laboratory research, not consumer VR/AR.<ref name="Robinson1963">Robinson, D.A. (1963). A method of measuring eye movement using a scleral search coil in a magnetic field. *IEEE Transactions on Biomedical Engineering, BME-10*(4), 137–145.</ref>
*'''[[Scleral search coil]]''': A wire coil embedded in a contact lens induces current in a surrounding magnetic field, giving sub-arc-minute precision—reserved for laboratory research.<ref name="Robinson1963">Robinson, D. A. (1963). A method of measuring eye movement using a scleral search coil in a magnetic field. ''IEEE Transactions on Biomedical Engineering, BME-10''(4), 137–145.</ref>


===Key Components (PCCR-based)===
===Key components (PCCR)===
A typical PCCR eye tracking system integrated into a VR/AR headset consists of:
* Infrared illuminators 
*'''Illuminators''': Infrared LEDs providing consistent, non-visible lighting.
* High-speed IR cameras
*'''Cameras''': Specialized high-speed infrared cameras positioned to capture clear images of both eyes.
* Real-time processing (onboard [[system-on-chip]] or discrete DSP) 
*'''Processing Unit''': Either onboard [[System on a Chip|SoC]] resources or dedicated hardware to run the detection and gaze calculation algorithms in real-time.
* Per-user calibration routines<ref name="Kar2017">Kar, A., &amp; Corcoran, P. (2017). A review and analysis of eye-gaze estimation systems, algorithms and performance evaluation methods in consumer platforms. ''IEEE Access, 5'', 16495–16519.</ref>
*'''Calibration Software''': [[Software]] routines guiding the user through calibration and storing individual profiles.<ref name="Kar2017">Kar, A., & Corcoran, P. (2017). A review and analysis of eye-gaze estimation systems, algorithms and performance evaluation methods in consumer platforms. *IEEE Access, 5*, 16495-16519.</ref>


===Eye Movement Types Measured===
===Eye-movement metrics===
Eye tracking systems in VR/AR can detect and analyze various types of eye movements and states:
Systems measure fixations, saccades, smooth pursuit, vergence, pupil diameter, and blink events, each informing attention, cognitive load, or depth cues.<ref name="Leigh2015">Leigh, R. J., &amp; Zee, D. S. (2015). ''The Neurology of Eye Movements''. Oxford University Press.</ref>
*'''[[Fixations]]''': Periods when the gaze remains relatively stable on a specific area (typically > 100-200 ms), indicating visual attention.
*'''[[Saccades]]''': Rapid, ballistic movements shifting the gaze between fixation points (typically 30-120 ms).
*'''[[Smooth pursuit]]''': Movements allowing the eyes to smoothly follow a moving object.
*'''[[Vergence]]''': The simultaneous movement of both eyes in opposite directions to obtain or maintain single binocular vision, critical for focusing on objects at different depths.
*'''[[Pupil dilation|Pupil Size / Dilation]]''': Changes in pupil diameter (pupillometry), which can correlate with changes in light levels, cognitive load, emotional arousal, or interest.<ref name="Leigh2015">Leigh, R. J., & Zee, D. S. (2015). *The neurology of eye movements*. Oxford University Press.</ref>
*'''Blinks''': Detection of eyelid closures.


==Applications in VR and AR==
==Applications in VR and AR==
Eye tracking unlocks numerous capabilities enhancing VR and AR experiences:
*'''[[Foveated rendering]]''' reduces GPU load by rendering full resolution only at the gaze locus.<ref name="Patney2016">Patney, A. et al. (2016). Towards foveated rendering for gaze-tracked VR. ''ACM Transactions on Graphics, 35''(6), 179.</ref><ref name="TobiiFoveated">[https://www.tobii.com/blog/eye-tracking-in-vr-a-vital-component Tobii Blog: Eye Tracking in VR — A Vital Component]. 2024-02-16.</ref><ref name="NvidiaFoveated">NVIDIA Corp. (n.d.). ''Maximize VR Performance with Foveated Rendering''. https://developer.nvidia.com/vrworks</ref>


*'''[[Foveated rendering]]''': Arguably the most impactful application for performance. By knowing precisely where the user is looking, the system renders the scene at maximum [[resolution]] only in the small, central area of the user's gaze (corresponding to the eye's high-acuity fovea), while rendering the peripheral areas at progressively lower resolutions. This mimics human vision and can drastically reduce the computational load on the [[graphics processing unit]] (GPU) – potentially by 30% to over 70% – allowing for higher fidelity graphics, increased frame rates, reduced [[latency]], or lower power consumption, all without a noticeable loss in perceived visual quality.<ref name="Patney2016">Patney, A., Salvi, M., Kim, J., Kaplanyan, A., Wyman, C., Benty, N., Luebke, D., & Lefohn, A. (2016). Towards foveated rendering for gaze-tracked virtual reality. *ACM Transactions on Graphics, 35*(6), 179.</ref><ref name="TobiiFoveated">[https://www.tobii.com/blog/eye-tracking-in-vr-a-vital-component Tobii Blog: Eye Tracking in VR — A Vital Component]. Retrieved Nov 17, 2023.</ref><ref name="NvidiaFoveated">NVIDIA Corporation. (n.d.). *Maximize VR Performance with Foveated Rendering*. NVIDIA Developer. Retrieved November 16, 2023, from https://developer.nvidia.com/vrworks/graphics/foveatedrendering</ref>
*'''Natural gaze interaction''' (point-and-confirm, dwell, or gesture) streamlines UI control.<ref name="Piumsomboon2017">Piumsomboon, T., Lee, G., Lindeman, R. W., &amp; Billinghurst, M. (2017). Exploring natural eye-gaze-based interaction for immersive VR. ''IEEE Symposium on 3D User Interfaces'', 36–39.</ref>


*'''Natural Interaction / Gaze-Based Interaction''': Eye tracking enables more intuitive control schemes:
*'''Social presence''' improves when trackers drive avatar eyes and facial expression.<ref name="MetaAvatarsEyeTracking">Meta Platforms, Inc. (2022, Oct 11). ''Meta Quest Pro: A New Way to Work, Create and Collaborate''. https://www.meta.com/blog/quest/meta-quest-pro-vr-headset-features-price-release-date/</ref>
**'''Gaze Selection/Pointing''': Allows users to select objects, menu items, or targets simply by looking at them. This is often combined with a confirmation action like a button press on a [[controller (computing)|controller]], a [[hand tracking]] gesture (for example pinch), or a short dwell time.<ref name="Piumsomboon2017">Piumsomboon, T., Lee, G., Lindeman, R. W., & Billinghurst, M. (2017). Exploring natural eye-gaze-based interaction for immersive virtual reality. *IEEE Symposium on 3D User Interfaces*, 36-39.</ref>
**'''Intent Prediction''': Systems can anticipate user actions or needs based on gaze patterns (for example highlighting an object the user looks at intently).
**'''Gaze-directed Locomotion''': Steering movement within the virtual world based on gaze direction.


*'''Enhanced Social Presence / [[Avatar]] Realism''': Realistic eye movements, including subtle saccades, blinks, and responsive gaze shifts, can be mirrored onto a user's avatar in social VR applications. This significantly enhances [[non-verbal communication]] and the feeling of [[social presence]] and connection when interacting with others.<ref name="MetaAvatarsEyeTracking">Meta Platforms, Inc. (2022, October 11). *Meta Quest Pro: A New Way to Work, Create and Collaborate*. Meta Quest Blog. https://www.meta.com/blog/quest/meta-quest-pro-vr-headset-features-price-release-date/</ref>
*'''Analytics &amp; research''': Heat-mapping, UX testing, and training assessment benefit from quantified gaze.<ref name="Clay2019">Clay, V., König, P., &amp; König, S. (2019). Eye tracking in virtual reality. ''Journal of Eye Movement Research, 12''(1).</ref>


*'''User Analytics and Research''': Eye tracking provides invaluable objective data for:
*'''Varifocal / adaptive-optics displays''' resolve the [[vergence-accommodation conflict]].<ref name="Akeley2004">Akeley, K., Watt, S. J., Girshick, A. R., &amp; Banks, M. S. (2004). A stereo display prototype with multiple focal distances. ''ACM Transactions on Graphics, 23''(3), 804–813.</ref>
**'''[[Usability testing]] & User Experience (UX) Research''': Understanding how users visually explore and interact with interfaces or environments.
**'''[[Attention mapping]]''': Creating heatmaps and gaze plots to visualize areas of interest and attention duration.
**'''Cognitive Load Assessment''': Measuring mental workload through metrics like pupil dilation, blink rate, and fixation patterns.<ref name="Clay2019">Clay, V., König, P., & König, S. (2019). Eye tracking in virtual reality. *Journal of Eye Movement Research, 12*(1).</ref>
**'''Training and Simulation Analysis''': Assessing trainee attention, situational awareness, and decision-making processes in professional simulations (for example medical, aviation).
 
*'''Automatic [[Interpupillary distance|IPD]] Adjustment''': Some headsets utilize the eye tracking cameras to automatically measure the user's interpupillary distance (the distance between the centers of the pupils) and mechanically adjust the lens spacing for optimal visual clarity, stereo depth perception, and user comfort.
 
*'''[[Accessibility]]''': Eye tracking offers a powerful hands-free input modality for users with limited physical mobility, enabling them to navigate interfaces, communicate (for example gaze typing), and control applications within VR/AR.
 
*'''Adaptive Optics / [[Varifocal display]]s''': Eye tracking is essential for dynamic varifocal displays, which adjust their focal plane based on where the user is looking in virtual depth. This helps address the [[vergence-accommodation conflict]], potentially reducing eye strain and improving visual realism.<ref name="Akeley2004">Akeley, K., Watt, S.J., Girshick, A.R., & Banks, M.S. (2004). A stereo display prototype with multiple focal distances. *ACM Transactions on Graphics, 23*(3), 804–813.</ref>
 
*'''[[Dynamic Distortion Compensation]]''': Real-time adjustments to lens distortion correction based on precise eye position relative to the lens center can improve perceived sharpness across the field of view.<ref name="TobiiFoveated"/>


==Current Implementations==
==Current Implementations==
Several commercially available VR and AR headsets incorporate integrated eye tracking:
===VR headsets with eye tracking===
*'''[[Apple Vision Pro]]''' – primary input modality and foveated rendering.<ref name="AppleVisionPro">Apple Inc. (n.d.). ''Apple Vision Pro''. https://www.apple.com/apple-vision-pro/</ref>
*'''[[Meta Quest Pro]]''' – inward sensors for rendering and avatar expression.<ref name="MetaAvatarsEyeTracking"/>
*'''[[PlayStation VR2]]''' – integrates Tobii tracking for games and foveation.<ref name="SonyPSVR2">Sony Interactive Entertainment. (2023). ''PS VR2 Features''. https://www.playstation.com/en-us/ps-vr2/ps-vr2-features/</ref>
*'''[[HTC VIVE Pro Eye]]''' – enterprise headset with 120 Hz Tobii tracking.<ref name="ViveProEye">HTC Corp. (n.d.). ''VIVE Pro Eye''. https://www.vive.com/us/product/vive-pro-eye/overview/</ref>
*'''[[Varjo]] XR-4 / XR-3 / VR-3 / Aero''' – 200 Hz research-grade tracking.<ref name="VarjoAero">Varjo Technologies Oy. (n.d.). ''Varjo Aero''. https://varjo.com/products/aero/</ref><ref name="VarjoXR3">Varjo Technologies. (2021). ''Varjo XR-3 Technical Specifications''. https://varjo.com/products/varjo-xr-3/</ref>
*'''[[Pimax]] Crystal''' – consumer 12K head-set with eye tracking for IPD and foveation.<ref name="PimaxCrystal">Pimax Technology (Shanghai) Co. Ltd. (n.d.). ''Pimax Crystal''. https://pimax.com/crystal/</ref>
*'''[[Pico 4 Enterprise]]''' (formerly Neo 3 Pro Eye) – Tobii-enabled enterprise unit.<ref name="PicoNeo3Eye">Pico Interactive. (2021). ''Pico Neo 3 Pro Eye Specifications''. https://pico-interactive.com/en/products/neo3-pro-eye</ref>
*'''[[HP Reverb G2 Omnicept Edition]]''' – eye, lip, and heart-rate sensors for training/analytics.<ref name="HPOmnicept">HP Dev. Co. L.P. (n.d.). ''HP Reverb G2 Omnicept Edition VR Headset''. https://www.hp.com/us-en/vr/reverb-g2-vr-headset-omnicept-edition.html</ref>


===VR Headsets with Eye Tracking===
===AR headsets===
*'''[[Apple Vision Pro]]''': Uses high-precision eye tracking as a primary input method (combined with hand gestures and voice), enabling UI navigation, selection, and foveated rendering.<ref name="AppleVisionPro">Apple Inc. (n.d.). *Apple Vision Pro*. Apple. Retrieved November 16, 2023, from https://www.apple.com/apple-vision-pro/</ref>
*'''[[Microsoft HoloLens 2]]''' – gaze-based targeting and automatic calibration.<ref name="HoloLens2">Microsoft Corp. (2019). ''HoloLens 2 Hardware Details''. https://learn.microsoft.com/hololens/hololens2-hardware</ref>
*'''[[Meta Quest Pro]]''': Features inward-facing sensors for eye and face tracking, primarily used for foveated rendering and driving realistic avatar expressions in social applications.<ref name="MetaAvatarsEyeTracking"/>
*'''[[Magic Leap 2]]''' – eye tracking for input, analytics, and segmented-display foveation.<ref name="MagicLeap2">Magic Leap, Inc. (2022). ''Magic Leap 2 Technical Overview''. https://www.magicleap.com/magic-leap-2</ref>
*'''[[PlayStation VR2]]''': Integrates [[Tobii]] eye tracking technology for foveated rendering, gaze-based interactions in games, and enhanced immersion.<ref name="SonyPSVR2">Sony Interactive Entertainment. (2023). *PS VR2 Features*. PlayStation.com. https://www.playstation.com/en-us/ps-vr2/features/</ref>
*'''[[HTC VIVE Pro Eye]]''': An earlier enterprise-focused headset integrating Tobii eye tracking (accuracy ~0.5-1.1 degrees, 120Hz). Newer VIVE models may offer eye tracking via add-ons (for example VIVE Focus 3 Eye Tracker).<ref name="ViveProEye">HTC Corporation. (n.d.). *VIVE Pro Eye*. VIVE. Retrieved November 16, 2023, from https://www.vive.com/us/product/vive-pro-eye/overview/</ref>
*'''[[Varjo]] XR-4, XR-3, VR-3, Aero''': High-end professional headsets featuring industrial-grade eye tracking (sub-degree accuracy, 200Hz) for demanding simulation, research, and design applications.<ref name="VarjoAero">Varjo Technologies Oy. (n.d.). *Varjo Aero*. Varjo. Retrieved November 16, 2023, from https://varjo.com/products/aero/</ref><ref name="VarjoXR3">Varjo Technologies. (2021). *Varjo XR-3 Technical Specifications*. Varjo.com.</ref>
*'''[[Pimax]] Crystal''': Consumer-focused high-resolution headset incorporating eye tracking for features like foveated rendering and automatic IPD adjustment.<ref name="PimaxCrystal">Pimax Technology (Shanghai) Co., Ltd. (n.d.). *Pimax Crystal*. Pimax. Retrieved November 16, 2023, from https://pimax.com/crystal/</ref>
*'''[[Pico 4 Enterprise]]''' (previously Pico Neo 3 Pro Eye): Enterprise headsets integrating Tobii eye tracking for business applications.<ref name="PicoNeo3Eye">Pico Interactive. (2021). *Pico Neo 3 Pro Eye Specifications*. Pico-interactive.com.</ref>
*'''[[HP Reverb G2 Omnicept Edition]]''': Featured integrated eye tracking (along with other biometric sensors) for enterprise use cases.<ref name="HPOmnicept">HP Development Company, L.P. (n.d.). *HP Reverb G2 Omnicept Edition VR Headset*. HP.com. Retrieved November 16, 2023, from https://www.hp.com/us-en/vr/reverb-g2-vr-headset-omnicept-edition.html</ref>


===AR Headsets with Eye Tracking===
==Technical specifications and performance metrics==
Eye tracking is also crucial for interaction and performance in AR:
;Accuracy
*'''[[Microsoft HoloLens 2]]''': Uses eye tracking for user calibration, interaction (gaze targeting), and potentially performance optimization. Reported accuracy around 1.5 degrees.<ref name="HoloLens2">Microsoft Corporation. (2019). *HoloLens 2 Hardware Details*. Microsoft.com.</ref>
:Mean angular error; consumer HMDs achieve 0.5°–1.5°.
*'''[[Magic Leap 2]]''': Incorporates eye tracking for input (gaze, dwell), foveated rendering (on segmented dimmer), user calibration, and analytics.<ref name="MagicLeap2">Magic Leap, Inc. (2022). *Magic Leap 2 Technical Overview*. MagicLeap.com.</ref>
;Precision
:RMS sample-to-sample variation, commonly 0.1°–0.5°.
;Sampling rate
:30 Hz–120 Hz (consumer) to >1000 Hz (research).
;Latency
:End-to-end delay ideally < 20 ms for foveated rendering; reported values 45–80 ms in some systems.<ref name="Mack2023">Mack, S., et al. (2023). Eye tracking in virtual reality: a broad review of applications and challenges. ''Virtual Reality, 27'', 1481–1505. https://link.springer.com/article/10.1007/s10055-022-00738-z</ref>
;Robustness
:Percentage of valid samples under motion, glasses, etc.
;Head box / eye box
:Spatial region within which tracking is maintained.<ref name="Blignaut2018">Blignaut, P. (2018). Using eye tracking to assess user experience: A case of a mobile banking application. In ''ACM International Conference Proceeding Series'', 219–228.</ref>


==Technical Specifications and Performance Metrics==
===Calibration methods===
===Key Performance Indicators===
*'''Point-by-point''' (static targets)
The quality and usability of eye tracking systems are measured using several critical metrics:
*'''Smooth-pursuit''' (moving target)
*'''Accuracy''': The average difference between the true gaze point and the system's reported gaze point, typically measured in degrees of visual angle. Good systems achieve 0.5° to 1.5° accuracy within the central field of view.
*'''Implicit / online refinement''' (during normal use) 
*'''Precision''': The consistency or reproducibility of measurements for the same gaze point (RMS of successive samples). Often between 0.1° and 0.5°.
Calibration can drift with headset slippage; repeated calibration or automatic re-calibration compensates.<ref name="Santini2017">Santini, T., Fuhl, W., &amp; Kasneci, E. (2017). CalibMe: Fast and unsupervised eye-tracker calibration for gaze-based pervasive HCI. ''CHI Conference on Human Factors in Computing Systems'', 2594–2605.</ref>
*'''Sampling Rate''': The frequency at which eye position is measured, expressed in Hertz (Hz). Consumer systems range from 30Hz to 120Hz or more, while research systems can exceed 1000Hz. Higher rates capture more detail about rapid movements like saccades.
*'''Latency''': The time delay between an actual eye movement and when it is detected and reported by the system. Crucial for real-time applications like foveated rendering and interaction, ideally below 20ms, though system latency can sometimes be higher (for example 45-81 ms reported in some studies).<ref name="SpringerReview2022">Mack, S., et al. (2022). A survey on eye tracking in virtual and augmented reality. *Virtual Reality*, 27, 1597–1625. https://link.springer.com/article/10.1007/s10055-022-00738-z</ref>
*'''Robustness / Tracking Ratio''': The percentage of time the system successfully tracks the eyes under various conditions (for example different users, lighting, eyewear).
*'''Field of View / Freedom of Head Movement''': The range of eye rotation and head position within which the system maintains tracking.
<ref name="Blignaut2018">Blignaut, P. (2018). Using eye tracking to assess user experience: A case of a mobile banking application. In *ACM International Conference Proceeding Series*, 219-228.</ref>
 
===Calibration Methods===
Achieving specified accuracy typically requires individual user calibration:
*'''Point Calibration''': The most common method. The user looks sequentially at several points displayed on the screen while the system records corresponding eye data to build a mapping model.
*'''Smooth Pursuit Calibration''': The user follows one or more moving targets across the screen.
*'''Implicit/Adjustment Calibration''': Systems that attempt to calibrate or refine calibration based on natural viewing behavior during normal use, potentially reducing user friction.
*'''Calibration Drift''': Accuracy can degrade over time due to headset slippage or physiological changes, potentially requiring recalibration. Some studies note significant drift (for example 30% accuracy loss) within minutes under certain conditions.<ref name="SpringerReview2022"/>
<ref name="Santini2018">Santini, T., Fuhl, W., & Kasneci, E. (2018). CalibMe: Fast and unsupervised eye tracker calibration for gaze-based pervasive human-computer interaction. *CHI Conference on Human Factors in Computing Systems*, 1-6.</ref>


==Challenges and Limitations==
==Challenges and Limitations==
Despite significant progress, eye tracking in VR/AR still faces hurdles:
===Technical===
 
* Inter-user anatomical variation 
===Technical Challenges===
* Glasses or contact-lens reflections 
*'''Individual Variations''': Differences in eye physiology (for example corneal curvature, pupil size, eyelid shape, ethnicity-related features, "droopy" eyelids) can impact tracking accuracy and robustness.
* Processing load and power budget 
*'''Eyewear Compatibility''': Prescription glasses (especially with thick lenses, bifocals, or certain coatings) and some types of contact lenses can interfere with IR illumination and camera imaging, degrading performance.
* Accuracy–latency trade-offs<ref name="Majaranta2014">Majaranta, P., &amp; Bulling, A. (2014). Eye tracking and eye-based human-computer interaction. In ''Advances in Physiological Computing'', 39–65. Springer.</ref>
*'''Processing Requirements''': Real-time, high-frequency eye tracking requires significant computational resources, impacting overall system performance and battery life, especially on standalone mobile headsets.
*'''Power Consumption''': The cameras and illuminators continuously consume power, contributing to battery drain in untethered devices.
*'''Accuracy/Latency Trade-offs''': Achieving both high accuracy and very low latency simultaneously remains challenging.
<ref name="Majaranta2014">Majaranta, P., & Bulling, A. (2014). Eye tracking and eye-based human-computer interaction. In *Advances in physiological computing*, 39-65. Springer.</ref>
 
===User Experience Concerns===
*'''Calibration Process''': The need for calibration can be perceived as inconvenient, especially if required frequently.
*'''[[Privacy]] Implications''': Eye tracking data is highly sensitive [[biometric data]]. It can potentially reveal information about identity, attention focus, cognitive state, emotional responses, interests, and even certain health conditions (for example fatigue, intoxication, neurological disorders), raising significant privacy concerns if collected, stored, or shared improperly.<ref name="Kroger2020">Kröger, J. L., Lutz, O. H. M., & Müller, F. (2020). What does your gaze reveal about you? On the privacy implications of eye tracking. *Privacy and Identity Management*, 226-241.</ref><ref name="EFFPrivacyVRAR">Crockford, K., & Electronic Frontier Foundation. (2020, November 19). *The Privacy Bird Isn't Real: Your VR/AR Data Is*. Electronic Frontier Foundation. https://www.eff.org/deeplinks/2020/11/privacy-bird-isnt-real-your-vrar-data</ref>
*'''The "[[Uncanny Valley]]" Effect''': Imperfectly synchronized or unnatural avatar eye movements can appear disturbing rather than enhance social presence.


===Accessibility Issues===
===User-experience===
*'''Compatibility with Eye Conditions''': Certain ophthalmological conditions (for example strabismus, nystagmus, ptosis, corneal scarring) can significantly impair or prevent accurate eye tracking for affected individuals.
* Calibration burden 
*'''Make-up Interference''': Certain types of mascara or eyeliner can sometimes interfere with tracking.
* Motion discomfort if latency is high 
* '''Privacy''': gaze reveals identity, intent, and health.<ref name="Kroger2020">Kröger, J. L., Lutz, O. H. M., &amp; Müller, F. (2020). What does your gaze reveal about you? On the privacy implications of eye tracking. In ''Privacy and Identity Management'', 226–241.</ref><ref name="EFFPrivacyVRAR">Crockford, K. (2020, Nov 19). ''The Privacy Bird Isn’t Real: Your VR/AR Data Is''. Electronic Frontier Foundation. https://www.eff.org/deeplinks/2020/11/privacy-bird-isnt-real-your-vrar-data</ref>


<ref name="Titz2018">Titz, J., Scholz, A., & Sedlmeier, P. (2018). Comparing eye trackers by correlating their eye-metric data. *Behavior Research Methods, 50*(5), 1853-1863.</ref>
===Accessibility===
* Eye conditions (strabismus, nystagmus) may defeat tracking 
* Cosmetic products can occlude IR glints<ref name="Titz2018">Titz, J., Scholz, A., &amp; Sedlmeier, P. (2018). Comparing eye trackers by correlating their eye-metric data. ''Behavior Research Methods, 50''(5), 1853–1863.</ref>


==Future Developments==
==Future Developments==
The field continues to evolve rapidly:
* Low-power, smaller sensors 
 
* Deep-learning-enhanced robustness (for example [[CNN]] pupil detectors)<ref name="Fuhl2016">Fuhl, W., Santini, T., Kasneci, G., &amp; Kasneci, E. (2016). PupilNet: Convolutional neural networks for robust pupil detection. ''CoRR, abs/1601.04902''.</ref>
===Emerging Technologies===
* Predictive gaze and perceptual super-sampling 
*'''Lower Power, Smaller Sensors''': Miniaturization and improved power efficiency for better integration into lighter HMDs.
* Emotion and cognitive-state inference<ref name="Duchowski2018">Duchowski, A. T., et al. (2018). The index of pupillary activity: Measuring cognitive load vis-à-vis task difficulty with pupil oscillation. ''CHI Conference on Human Factors in Computing Systems'', 1–13.</ref>
*'''[[Artificial intelligence|AI]]/[[Machine learning|ML]]-Enhanced Tracking''': Using neural networks to improve robustness across diverse users, reduce calibration needs, and potentially infer more complex states from eye data.<ref name="Fuhl2016">Fuhl, W., Santini, T., Kasneci, G., & Kasneci, E. (2016). PupilNet: Convolutional neural networks for robust pupil detection. *CoRR, abs/1601.04902*.</ref>
*'''Advanced Optical Designs''': Novel lens and camera configurations to improve tracking quality, especially for users with eyewear.
*'''Sensor Fusion''': Combining eye tracking data with other sensor inputs (for example [[electroencephalography]] (EEG), head tracking, facial expression tracking) for richer interaction and analysis.
 
===Research Directions===
*'''Predictive Tracking''': Algorithms that anticipate eye movements to compensate for system latency, enabling smoother foveated rendering and interaction.
*'''Emotion and Cognitive State Recognition''': Refining models to reliably infer affective states or cognitive load from eye metrics for adaptive interfaces or mental health applications.
*'''Standardization''': Developing common metrics, APIs, and data formats to facilitate cross-platform development and research comparison.
*'''Longitudinal Tracking''': Understanding how gaze patterns change over extended use and adapting systems accordingly.
<ref name="Duchowski2018">Duchowski, A. T., Krejtz, K., Krejtz, I., Biele, C., Niedzielska, A., Kiefer, P., Raubal, M., & Giannopoulos, I. (2018). The index of pupillary activity: Measuring cognitive load vis-à-vis task difficulty with pupil oscillation. *CHI Conference on Human Factors in Computing Systems*, 1-13.</ref>


==Software Development and APIs==
==Software Development and APIs==
Integrating eye tracking into applications requires specific software support:
Major toolkits include Unity XR Interaction Toolkit, Unreal Engine Eye Tracker Interface, and [[OpenXR]] (`XR_EXT_eye_gaze_interaction`). Vendors such as Tobii supply dedicated SDKs.<ref name="TobiiSDK">Tobii Technology. (2023). ''Tobii XR SDK Documentation''. https://vr.tobii.com/sdk/</ref> Depending on the API layer, developers can access raw eye vectors, classified events (fixation/saccade), or semantic object-gaze hits.<ref name="Kumar2016">Kumar, D., Dutta, A., Das, A., &amp; Lahiri, U. (2016). SmartEye: Developing a novel eye-tracking system for quantitative assessment of oculomotor abnormalities. ''IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24''(10), 1051–1059.</ref>
 
===Development Frameworks and APIs===
*'''[[Unity (game engine)|Unity]]''': Provides APIs through its XR Interaction Toolkit and potential vendor-specific SDKs (for example Tobii XR SDK).
*'''[[Unreal Engine]]''': Offers native interfaces and plugins for accessing eye tracking data.
*'''[[OpenXR]]''': The cross-platform standard includes an `XR_EXT_eye_gaze_interaction` extension, allowing developers to write more portable code.
*'''Vendor SDKs''': Companies like [[Tobii]] provide dedicated Software Development Kits offering fine-grained control and optimized features for their hardware.<ref name="TobiiSDK">Tobii Technology. (2023). *Tobii XR SDK Documentation*. Tobii.com.</ref>
 
===Data Access Levels===
Developers typically access eye tracking data at different levels of abstraction:
*'''Raw Data''': Direct coordinates of pupil centers, glints, eye openness, etc. Requires significant processing by the application.
*'''Gaze Data''': Processed output providing calibrated gaze origin and direction vectors, or intersection points on virtual surfaces.
*'''Eye Movement Events''': Classified data identifying fixations, saccades, and blinks.
*'''Semantic Data''': Higher-level interpretations, such as the specific object being looked at (gaze target) or estimated attention levels.
<ref name="Kumar2016">Kumar, D., Dutta, A., Das, A., & Lahiri, U. (2016). SmartEye: Developing a novel eye tracking system for quantitative assessment of oculomotor abnormalities. *IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24*(10), 1051-1059.</ref>


==Ethical and Privacy Considerations==
==Ethical and Privacy Considerations==
The collection and use of eye tracking data necessitate careful ethical consideration:
Gaze data are treated as sensitive biometric information under GDPR, CCPA/CPRA, and similar regulations. Best practice requires informed consent, minimal data retention, encryption in transit and at rest, and transparency around secondary uses.<ref name="Kroger2020"/>
 
*'''Biometric Data Privacy''': Gaze patterns can be unique identifiers. This data requires strong [[data security]] measures and compliance with regulations like [[GDPR]] (which may classify it as sensitive biometric data).
*'''Inference and Profiling''': The potential to infer sensitive information (health, emotions, interests) without explicit consent raises ethical questions.
*'''Attention Monitoring''': Concerns about workplace surveillance or manipulative advertising based on attention analytics.
*'''Informed Consent''': Users must be clearly informed about what eye data is collected, how it is processed, stored, and shared, and for what purposes. Opt-in mechanisms are crucial.
*'''Data Minimization and Anonymization''': Collect only necessary data, anonymize or aggregate whenever possible, and define clear data retention policies.
<ref name="Kroger2020"/>


==Standards and Regulations==
==Standards and Regulations==
Efforts are underway to standardize aspects of eye tracking technology and address its implications:
*'''[[Khronos Group]]''' – OpenXR extensions 
*'''[[Khronos Group]] / [[OpenXR]]''': Defines standard APIs for accessing eye tracking data (for example [[XR_EXT_eye_gaze_interaction]]).
*'''IEEE''' – draft standard P2048.5 for XR learning metrics
*'''[[IEEE Standards Association|IEEE]]''': Working groups like IEEE P2048.5 focus on standards for immersive learning, potentially including eye tracking metrics.
*'''VRIF''' – implementation guidelines
*'''[[Virtual Reality Industry Forum]] (VRIF)''': Develops guidelines for VR implementation, potentially covering eye tracking best practices.
*'''ISO/IEC JTC 1/SC 24''' – graphics and XR data standards<ref name="ISO_SC24">International Organization for Standardization. (2023). ''ISO/IEC JTC 1/SC 24 – Computer graphics, image processing and environmental data representation''. ISO.org.</ref>
*'''Data Protection Regulations''': [[GDPR]] (Europe), [[CCPA]]/[[CPRA]] (California), and similar laws globally impose requirements on handling personal and biometric data, including eye tracking data.
*'''[[ISO]]/[[IEC]] JTC 1/SC 24''': Committee working on international standards for computer graphics, image processing, and environmental data representation, relevant to VR/AR interfaces.<ref name="ISO_SC24">International Organization for Standardization. (2023). *ISO/IEC JTC 1/SC 24 - Computer graphics, image processing and environmental data representation*. ISO.org.</ref>


==See Also==
==See also==
*   [[Augmented reality]]
* [[Augmented reality]]
*   [[AR headset]]
* [[AR headset]]
*   [[Attention mapping]]
* [[Attention mapping]]
*   [[Avatar]]
* [[Avatar]]
*   [[Computer vision]]
* [[Computer vision]]
*   [[Foveated rendering]]
* [[Foveated rendering]]
*   [[Gaze]]
* [[Gaze]]
*   [[Hand tracking]]
* [[Hand tracking]]
*   [[Head-mounted display]]
* [[Head-mounted display]]
*   [[Human eye]]
* [[Infrared]]
*  [[Human–computer interaction]]
* [[Interpupillary distance]]
*  [[Infrared]]
* [[Latency]]
*   [[Interpupillary distance]]
* [[Privacy]] in VR/AR
*   [[Latency]]
* [[Saccade]]
*   [[Privacy]] in VR/AR
* [[Social presence]]
*   [[Pupil]]
* [[Varifocal display]]
*  [[Saccade]]
* [[Vergence-accommodation conflict]]
*   [[Social presence]]
* [[Virtual reality]]
*   [[Tobii]]
* [[VR headset]]
*  [[Varifocal display]]
*   [[Vergence-accommodation conflict]]
*   [[Virtual reality]]
*   [[VR headset]]


==References==
==References==
<references />
<references />

Revision as of 16:21, 1 May 2025

See also: Terms and Technical Terms

Eye tracking is a sensor technology that measures eye positions and eye movement. In the context of virtual reality (VR) and augmented reality (AR), it refers to the integration of sensors within VR headsets or AR headsets to determine precisely where the user is looking (gaze) in real time inside a virtual environment or across an overlaid digital interface. By accurately monitoring characteristics such as pupil position, corneal reflections, and eye movements (for example saccades and fixations), the technology enables more immersive, efficient, and intuitive user experiences. Eye tracking has become a critical feature of modern head-mounted displays (HMDs), driving advances in rendering, interaction, analytics, and social presence.[1]

History

Systematic study of eye movements began in the 19th century. Louis Émile Javal observed in 1879 that reading involves discrete fixations and rapid saccades.[2] Early instruments included Edmund Huey’s contact-lens tracker (≈1908),[3] Guy T. Buswell’s film trackers in the 1930s, and Alfred L. Yarbus’s seminal work on task-dependent gaze in the 1960s.[4] Video-based systems emerged in the 1970s, while compact infrared trackers suitable for HMDs appeared in the 2010s.

Technical Principles

Tracking methods

Most contemporary HMDs use one of four foundations:

  • Pupil-centre/corneal-reflection (PCCR): Infrared (IR) LEDs illuminate the eye; IR cameras capture images; computer-vision algorithms locate the pupil centre and one or more corneal “glints”; the vector between them yields 3-D gaze.[5][6]
  • Video-feature tracking: Cameras analyse iris texture, pupil outline, or eye-surface vessels; PCCR can be treated as a subset.[7]
  • Electrooculography (EOG): Electrodes around the eye sense corneo-retinal potential changes; useful for coarse movements or eyelid activity when cameras are unsuitable.[8]
  • Scleral search coil: A wire coil embedded in a contact lens induces current in a surrounding magnetic field, giving sub-arc-minute precision—reserved for laboratory research.[9]

Key components (PCCR)

  • Infrared illuminators
  • High-speed IR cameras
  • Real-time processing (onboard system-on-chip or discrete DSP)
  • Per-user calibration routines[10]

Eye-movement metrics

Systems measure fixations, saccades, smooth pursuit, vergence, pupil diameter, and blink events, each informing attention, cognitive load, or depth cues.[11]

Applications in VR and AR

  • Natural gaze interaction (point-and-confirm, dwell, or gesture) streamlines UI control.[15]
  • Social presence improves when trackers drive avatar eyes and facial expression.[16]
  • Analytics & research: Heat-mapping, UX testing, and training assessment benefit from quantified gaze.[17]

Current Implementations

VR headsets with eye tracking

AR headsets

Technical specifications and performance metrics

Accuracy
Mean angular error; consumer HMDs achieve 0.5°–1.5°.
Precision
RMS sample-to-sample variation, commonly 0.1°–0.5°.
Sampling rate
30 Hz–120 Hz (consumer) to >1000 Hz (research).
Latency
End-to-end delay ideally < 20 ms for foveated rendering; reported values 45–80 ms in some systems.[29]
Robustness
Percentage of valid samples under motion, glasses, etc.
Head box / eye box
Spatial region within which tracking is maintained.[30]

Calibration methods

  • Point-by-point (static targets)
  • Smooth-pursuit (moving target)
  • Implicit / online refinement (during normal use)

Calibration can drift with headset slippage; repeated calibration or automatic re-calibration compensates.[31]

Challenges and Limitations

Technical

  • Inter-user anatomical variation
  • Glasses or contact-lens reflections
  • Processing load and power budget
  • Accuracy–latency trade-offs[32]

User-experience

  • Calibration burden
  • Motion discomfort if latency is high
  • Privacy: gaze reveals identity, intent, and health.[33][34]

Accessibility

  • Eye conditions (strabismus, nystagmus) may defeat tracking
  • Cosmetic products can occlude IR glints[35]

Future Developments

  • Low-power, smaller sensors
  • Deep-learning-enhanced robustness (for example CNN pupil detectors)[36]
  • Predictive gaze and perceptual super-sampling
  • Emotion and cognitive-state inference[37]

Software Development and APIs

Major toolkits include Unity XR Interaction Toolkit, Unreal Engine Eye Tracker Interface, and OpenXR (`XR_EXT_eye_gaze_interaction`). Vendors such as Tobii supply dedicated SDKs.[38] Depending on the API layer, developers can access raw eye vectors, classified events (fixation/saccade), or semantic object-gaze hits.[39]

Ethical and Privacy Considerations

Gaze data are treated as sensitive biometric information under GDPR, CCPA/CPRA, and similar regulations. Best practice requires informed consent, minimal data retention, encryption in transit and at rest, and transparency around secondary uses.[33]

Standards and Regulations

  • Khronos Group – OpenXR extensions
  • IEEE – draft standard P2048.5 for XR learning metrics
  • VRIF – implementation guidelines
  • ISO/IEC JTC 1/SC 24 – graphics and XR data standards[40]

See also

References

  1. ↑ Duchowski, A. T. (2017). Eye Tracking Methodology: Theory and Practice. Springer.
  2. ↑ Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372–422.
  3. ↑ Huey, E. B. (1908). The Psychology and Pedagogy of Reading. Macmillan.
  4. ↑ Yarbus, A. L. (1967). Eye Movements and Vision. Plenum Press.
  5. ↑ Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Weijer, J. (2011). Eye Tracking: A Comprehensive Guide to Methods and Measures. Oxford University Press.
  6. ↑ Lang, B. (2023, May 2). Eye-tracking Is a Game-Changer for XR That Goes Far Beyond Foveated Rendering. Road to VR. https://www.roadtovr.com/why-eye-tracking-is-a-game-changer-for-vr-headsets-virtual-reality/
  7. ↑ Hansen, D. W., & Ji, Q. (2010). In the eye of the beholder: A survey of models for eyes and gaze. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 478–500.
  8. ↑ Bulling, A., Ward, J. A., Gellersen, H., & Tröster, G. (2011). Eye movement analysis for activity recognition using electrooculography. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(4), 741–753.
  9. ↑ Robinson, D. A. (1963). A method of measuring eye movement using a scleral search coil in a magnetic field. IEEE Transactions on Biomedical Engineering, BME-10(4), 137–145.
  10. ↑ Kar, A., & Corcoran, P. (2017). A review and analysis of eye-gaze estimation systems, algorithms and performance evaluation methods in consumer platforms. IEEE Access, 5, 16495–16519.
  11. ↑ Leigh, R. J., & Zee, D. S. (2015). The Neurology of Eye Movements. Oxford University Press.
  12. ↑ Patney, A. et al. (2016). Towards foveated rendering for gaze-tracked VR. ACM Transactions on Graphics, 35(6), 179.
  13. ↑ Tobii Blog: Eye Tracking in VR — A Vital Component. 2024-02-16.
  14. ↑ NVIDIA Corp. (n.d.). Maximize VR Performance with Foveated Rendering. https://developer.nvidia.com/vrworks
  15. ↑ Piumsomboon, T., Lee, G., Lindeman, R. W., & Billinghurst, M. (2017). Exploring natural eye-gaze-based interaction for immersive VR. IEEE Symposium on 3D User Interfaces, 36–39.
  16. ↑ 16.0 16.1 Meta Platforms, Inc. (2022, Oct 11). Meta Quest Pro: A New Way to Work, Create and Collaborate. https://www.meta.com/blog/quest/meta-quest-pro-vr-headset-features-price-release-date/
  17. ↑ Clay, V., König, P., & König, S. (2019). Eye tracking in virtual reality. Journal of Eye Movement Research, 12(1).
  18. ↑ Akeley, K., Watt, S. J., Girshick, A. R., & Banks, M. S. (2004). A stereo display prototype with multiple focal distances. ACM Transactions on Graphics, 23(3), 804–813.
  19. ↑ Apple Inc. (n.d.). Apple Vision Pro. https://www.apple.com/apple-vision-pro/
  20. ↑ Sony Interactive Entertainment. (2023). PS VR2 Features. https://www.playstation.com/en-us/ps-vr2/ps-vr2-features/
  21. ↑ HTC Corp. (n.d.). VIVE Pro Eye. https://www.vive.com/us/product/vive-pro-eye/overview/
  22. ↑ Varjo Technologies Oy. (n.d.). Varjo Aero. https://varjo.com/products/aero/
  23. ↑ Varjo Technologies. (2021). Varjo XR-3 Technical Specifications. https://varjo.com/products/varjo-xr-3/
  24. ↑ Pimax Technology (Shanghai) Co. Ltd. (n.d.). Pimax Crystal. https://pimax.com/crystal/
  25. ↑ Pico Interactive. (2021). Pico Neo 3 Pro Eye Specifications. https://pico-interactive.com/en/products/neo3-pro-eye
  26. ↑ HP Dev. Co. L.P. (n.d.). HP Reverb G2 Omnicept Edition VR Headset. https://www.hp.com/us-en/vr/reverb-g2-vr-headset-omnicept-edition.html
  27. ↑ Microsoft Corp. (2019). HoloLens 2 Hardware Details. https://learn.microsoft.com/hololens/hololens2-hardware
  28. ↑ Magic Leap, Inc. (2022). Magic Leap 2 Technical Overview. https://www.magicleap.com/magic-leap-2
  29. ↑ Mack, S., et al. (2023). Eye tracking in virtual reality: a broad review of applications and challenges. Virtual Reality, 27, 1481–1505. https://link.springer.com/article/10.1007/s10055-022-00738-z
  30. ↑ Blignaut, P. (2018). Using eye tracking to assess user experience: A case of a mobile banking application. In ACM International Conference Proceeding Series, 219–228.
  31. ↑ Santini, T., Fuhl, W., & Kasneci, E. (2017). CalibMe: Fast and unsupervised eye-tracker calibration for gaze-based pervasive HCI. CHI Conference on Human Factors in Computing Systems, 2594–2605.
  32. ↑ Majaranta, P., & Bulling, A. (2014). Eye tracking and eye-based human-computer interaction. In Advances in Physiological Computing, 39–65. Springer.
  33. ↑ 33.0 33.1 Kröger, J. L., Lutz, O. H. M., & Müller, F. (2020). What does your gaze reveal about you? On the privacy implications of eye tracking. In Privacy and Identity Management, 226–241.
  34. ↑ Crockford, K. (2020, Nov 19). The Privacy Bird Isn’t Real: Your VR/AR Data Is. Electronic Frontier Foundation. https://www.eff.org/deeplinks/2020/11/privacy-bird-isnt-real-your-vrar-data
  35. ↑ Titz, J., Scholz, A., & Sedlmeier, P. (2018). Comparing eye trackers by correlating their eye-metric data. Behavior Research Methods, 50(5), 1853–1863.
  36. ↑ Fuhl, W., Santini, T., Kasneci, G., & Kasneci, E. (2016). PupilNet: Convolutional neural networks for robust pupil detection. CoRR, abs/1601.04902.
  37. ↑ Duchowski, A. T., et al. (2018). The index of pupillary activity: Measuring cognitive load vis-à-vis task difficulty with pupil oscillation. CHI Conference on Human Factors in Computing Systems, 1–13.
  38. ↑ Tobii Technology. (2023). Tobii XR SDK Documentation. https://vr.tobii.com/sdk/
  39. ↑ Kumar, D., Dutta, A., Das, A., & Lahiri, U. (2016). SmartEye: Developing a novel eye-tracking system for quantitative assessment of oculomotor abnormalities. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(10), 1051–1059.
  40. ↑ International Organization for Standardization. (2023). ISO/IEC JTC 1/SC 24 – Computer graphics, image processing and environmental data representation. ISO.org.
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