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Walking-in-place

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Walking-in-place (WIP), also written walking in place and, in consumer software, often called running in place, is a locomotion technique for virtual reality in which the user steps on the spot while a sensing system converts the stepping motion into forward travel through the virtual environment. The user's body stays within the tracked area, so the technique lets a person cross virtual distances far larger than the physical room without a treadmill or other mechanical aid.[1][2]

The technique was introduced to VR research by Mel Slater, Martin Usoh and Anthony Steed, whose 1995 paper "Taking steps" used a neural network on head-mounted display tracking data to recognise stepping and reported higher subjective presence than the hand-pointing "flying" navigation common at the time.[1] A 1999 replication by University College London and the University of North Carolina at Chapel Hill added real walking as a third condition and gave the field its shorthand ranking, "Walking > Walking-in-Place > Flying".[3] Later research systems such as Gaiter (1999), LLCM-WIP (2008) and GUD WIP (2010) moved step sensing from the head to the legs and reduced the latency between a step and the resulting virtual motion.[4][2][5]

With the arrival of consumer headsets in 2016, hobbyist and commercial tools brought the idea to SteamVR and Meta Quest games that were built for thumbstick movement: RIPmotion and PocketStrafe (2016), Natural Locomotion (2018), the Cybershoes seated foot controller (2018) and VRocker (2020).[6][7][8][9][10] Studies since 1995 have found that walking-in-place produces more presence than joystick "flying", while recent comparisons rate teleportation as easier to use and less likely to cause cybersickness.[3][11]

Reviewed 20 September 2026. Research history, study designs and results, product release dates, prices, company status and every citation. About review dates.

How it works

A walking-in-place system senses some part of the body, detects when the user is stepping, and maps that motion to viewpoint movement. Feasel, Whitton and Wendt noted in 2008 that published descriptions of such systems had concentrated on step detection and rarely explained how detected movement was turned into viewpoint motion, so they reconstructed the earlier methods from conversations with their developers.[2]

Sensing steps

The original technique needed no extra hardware. Slater, Usoh and Steed fed the stream of position coordinates from the head-mounted display's tracker into a feed-forward neural network trained to distinguish walking on the spot from any other head movement; whenever the network reported walking, the participant was moved forward in the direction of gaze.[1] The 1999 replication streamed head position to the same network at 10 Hz and found that a single network trained on one person's gait recognised the movements of casual visitors without individual training. The authors also described the network's two failure modes: judging users to be walking when they are not, which produced overshoot and virtual collisions when stopping, and failing to detect walking, which showed up as a momentary pause at the start of movement.[3]

Later systems instrumented the legs. Gaiter, developed by James Templeman's group at the Naval Research Laboratory, sensed leg movement so that the legs themselves determined the direction, extent and timing of virtual steps; it could tell in-place stepping gestures from real steps, so both could be mixed, and the user could walk straight, turn on the spot or turn while advancing in a body-centred coordinate frame.[4] According to Feasel and colleagues, Gaiter analysed the horizontal components of knee motion and had to wait for the knee to reach its point of maximal extent before a virtual step was recognised, which cost about half a step (roughly 400 ms at a moderate pace).[2] LLCM-WIP at UNC Chapel Hill instead tracked the vertical speed of the heels; because the laboratory's magnetic trackers could not be worn on the feet over a metal floor, the trackers sat on the shins just below the knee and a fixed rigid offset estimated the heel position, with a chest tracker supplying the direction of travel.[2] GUD WIP used an eight-camera PhaseSpace optical motion-capture system with seven LEDs on a plate attached to each shin, and a state machine that tracked the phases of the gait cycle from foot-off to foot-strike.[5]

Cheaper sensors followed. Shake-Your-Head (2010) detected head movements with a basic webcam so that a seated desktop user could walk, turn, jump and crawl through a virtual world.[12] VR-STEP (2016) used only a smartphone's inertial sensors as a real-time pedometer for hands-free navigation in mobile VR.[13] Consumer implementations use whatever the headset already tracks: the position of the hand controllers held at the hips, the movement of the player's body as seen by a standard headset and controllers, a phone in a pocket, or optional Vive Trackers or game-console controllers strapped to the ankles.[6][7][8][10] A 2022 CHI study of consumer-accessible setups compared four such sensing choices, which its authors called Head-bob, Arm-swing, Leg-lift and Full-body, using an HTC Vive Pro headset and Vive Trackers on the arms, ankles and waist with 40 participants in a commuting simulation.[14][15]

Mapping steps to motion

How detected steps become viewpoint motion determines whether the result feels smooth or jerky. Feasel and colleagues report that in the original neural-network system the viewpoint jumped forward by a full step length between one frame and the next, and that a later revision spread each step's displacement evenly over several frames, which started promptly but ended abruptly and felt like a series of forward glides.[2] LLCM-WIP replaced step counting with a continuous mapping: the vertical heel speed, summed over both feet, was smoothed, offset and scaled into a locomotion speed that was updated every frame. Subtracting a constant offset meant that slow, low heel movements, such as turning on the spot or shuffling a short distance, did not register as travel, so real-world turning and short maneuvering could be combined with virtual walking. The measured starting latency averaged 138 ms and the stopping latency 96 ms, less than one eighth of a gait cycle.[2]

GUD WIP took a biomechanical approach. Because step frequency and step length are correlated in real walking, and step length scales with height, the system measured step frequency at several points within each in-place step and combined it with the user's height to produce a speed that matched real walking for the same cadence. In a user study its output speeds were more consistent with step frequency and showed less within-step fluctuation than LLCM-WIP, at the cost of roughly 500 ms of stopping latency, since a pause in stepping cannot be distinguished from the double-support phase of a normal stride until a frequency threshold is crossed.[5] Perceptual work by Nilsson, Serafin and Nordahl asked how fast the virtual world should move for a given stepping cadence: two within-subjects studies exposed participants to visual gains from 1.0 to 3.0 times their estimated normal walking speed and used the results to propose a range of perceptually natural walking speeds for WIP gestures.[16] Later proposals include controlling virtual speed by step height as well as step frequency, an elastic band that resists lifting the foot, and pseudo-haptic feedback to make virtual slopes feel steeper.[17]

Direction

Systems differ in what sets the direction of travel. The 1995 technique and its 1999 replication moved the user along the head's facing direction, which the UNC and UCL authors found intuitive enough that participants navigated without being told how it worked.[3] Gaiter took direction from the legs, so a user could step sideways or backwards, and LLCM-WIP used a chest-mounted orientation tracker so that the head could look around freely while walking.[4][2] RIPmotion moves the player in the direction the hips point, taken from controllers held at the hips.[6]

History

Research origins

Slater, Usoh and Steed presented walking-in-place in ACM Transactions on Computer-Human Interaction in September 1995. The paper argued that presence should increase as proprioceptive information from body movement matches the sensory feedback from the displays, described the neural-network step detector, and reported two experiments comparing the technique with hand-pointing navigation. Presence ratings were higher for the walking method, provided participants subjectively associated with the virtual body they were given; the paper also applied the technique to climbing virtual steps and ladders.[1] Contemporary work by the same group referred to the approach as a "virtual treadmill".[3]

In 1999 Usoh, Arthur, Whitton, Bastos, Steed, Slater and Frederick P. Brooks Jr. replicated the study at UNC Chapel Hill with a ceiling-mounted optical tracker covering roughly 10 m by 4 m, which made real walking possible as a third condition. Thirty-three naive participants were split into flyers, virtual walkers and real walkers (each group six men and five women), with eleven experienced users added as a check on expertise. Real walking was rated significantly easier than both virtual walking and flying; the largest difference in presence was between flyers and both kinds of walkers; real walkers reported higher presence than virtual walkers, but the difference was significant only in some statistical models. The system's overall latency was about 100 ms, with an additional lag of about 500 ms for walking-in-place, and the authors noted that follow-on work detected footfalls with a head-mounted accelerometer.[3] In 2024 a group including Whitton and Steed described the two studies as seminal for locomotion research and called for coordinated replications with current headsets and with teleportation added as a condition.[18]

Templeman, Denbrook and Sibert published Gaiter in Presence in December 1999, framing it as the outcome of an analysis of what a locomotion control needs so that moving through a virtual environment resembles walking through the real one.[4] The UNC group's LLCM-WIP followed at the IEEE Symposium on 3D User Interfaces in 2008, and GUD WIP at IEEE Virtual Reality in 2010.[2][5] Shake-Your-Head, presented at VRST 2010 by Léo Terziman, Maud Marchal, Anatole Lécuyer and colleagues, extended the idea to seated desktop VR with webcam head tracking; in its experiment the technique allowed faster navigation than conventional input devices after a short learning period and was rated more fun and more presence-inducing.[12] By 2016 Nilsson, Serafin and Nordahl could review the field and argue that the next problem was perceived naturalness, the degree to which WIP feels like real walking, rather than detection alone.[19]

Year System Institution Sensing Notable contribution
1995 Walking technique ("virtual treadmill") Slater, Usoh, Steed Neural network on HMD tracker positions First presence study of WIP versus hand-pointing flight[1]
1999 Replication with real walking UCL and UNC Chapel Hill Same neural network, 10 Hz, wide-area ceiling tracker Walking > walking-in-place > flying ranking[3]
1999 Gaiter Naval Research Laboratory Leg (knee) motion sensing Legs set direction, extent and timing; real and gestural steps can be mixed[4][2]
2008 LLCM-WIP UNC Chapel Hill Magnetic trackers on shins estimating heel speed, chest orientation tracker Continuous per-frame speed; 138 ms start and 96 ms stop latency[2]
2010 GUD WIP UNC Chapel Hill Eight-camera optical motion capture on shins Speed from step frequency and height using gait biomechanics[5]
2010 Shake-Your-Head Terziman, Marchal, Emily, Multon, Arnaldi, Lecuyer Webcam head tracking Seated desktop WIP with turning, jumping and crawling[12]
2016 VR-STEP Tregillus and Folmer Smartphone inertial sensors Hands-free pedometry for mobile VR; 18-user study against auto-walk[13]

Consumer implementations

Consumer VR arrived in 2016 with room-scale tracking but only a few square metres of floor, and many games shipped with either teleportation or thumbstick "smooth" locomotion. Independent developers responded with running-in-place tools that emulate the thumbstick so that existing games need no modification. The first widely shared example was RIPmotion ("Running In Place motion"), which Ryan Sullivan published as a test scene for the HTC Vive on 17 April 2016: the player holds both controllers at the hips, presses both touchpads and runs on the spot, travelling in the direction the hips point, with a "lazy mode" that moves the player without stepping.[6] UploadVR covered the technique in July 2016, noting that an earlier RIPmotion iteration had fastened a controller to the belt to track the waist, alongside similar hobbyist projects and the open-source ArmSwinger system, which drives movement from arm swinging rather than steps.[20] PocketStrafe, from the developer Cool Font, put the sensor in the user's pocket: an iOS or Android app connected to the PC over Wi-Fi and translated running in place into movement input for Oculus Rift and HTC Vive games, leaving both hands and controllers free; it launched in December 2016 at a promotional price of US$0.99.[7]

Natural Locomotion, by the developer Myou, was released on Steam on 7 April 2018. It emulates trackpad or stick input in any SteamVR game that supports it, using either arm swinging or motion sensors on the feet, and ships with profiles for more than 100 games; supported foot sensors include Vive Trackers, Nintendo Switch Joy-Con and PlayStation 3 Move controllers, and Android phones with an accelerometer, gyroscope and magnetometer. As of September 2026 it is listed at US$9.99.[8] Feet-tracker support entered beta on 24 December 2018, walking in place with smartphones was added on 28 June 2019, and the developer announced Quest 2 compatibility on 13 October 2020.[21][22][23] VRocker, by digitalsoulVR, was released on Steam on 18 June 2020 (US$10.99 in September 2026). It installs a SteamVR driver that overrides a game's joystick smooth locomotion and converts the player's body movement into walking or running, so the player can run in place, swing the arms, sway, step, squat, rock or lunge without extra hardware; the developer was still shipping updates in August 2026.[10][24]

Cybershoes, from a Vienna company of the same name, took a seated approach. The user straps roller-equipped soles over their shoes, sits on a swivel chair and slides the feet back and forth across the floor; a wheel under each shoe converts the motion into emulated button presses for PC VR games with free locomotion. The Kickstarter campaign launched in October 2018 with a goal of 30,000 euro and passed 100,000 euro within about a day at pledge tiers of 151 euro and 193 euro; the wireless receiver connected by USB, and the company quoted 8 to 10 hours of play per three-hour charge.[9] A second Kickstarter in 2020 funded a Bluetooth version for the standalone Meta Quest and raised roughly US$98,000 from 470 backers.[25] Production stopped around 2023, the US company Cybershoes Inc. ceased operating in 2024, and Cybershoes GmbH was shut down in April 2025; the company's website describes the product as retired.[25][26][27]

Product Developer Released Sensing Platforms Status (September 2026)
RIPmotion Ryan Sullivan April 2016 Controllers held at the hips HTC Vive (SteamVR) Free test scene and developer script[6]
PocketStrafe Cool Font December 2016 Smartphone in a pocket, Wi-Fi to PC Oculus Rift, HTC Vive Launched at US$0.99[7]
Natural Locomotion Myou 7 April 2018 Arm swing, or trackers and phones on the feet SteamVR (HTC Vive, Oculus Rift and Quest, Windows Mixed Reality, Valve Index, Pimax, Pico Neo 2) Sold on Steam, US$9.99[8]
Cybershoes Cybershoes GmbH Kickstarter October 2018; Quest version 2020 Rollers and sensors in shoe attachments, seated SteamVR; Meta Quest Company closed April 2025, product retired[9][25]
VRocker digitalsoulVR 18 June 2020 Body movement (running in place, arm swinging, swaying, stepping, squatting, rocking, lunging), no extra hardware SteamVR Sold on Steam, US$10.99, updated August 2026[10][24]

Comparison with other locomotion methods

In Costas Boletsis's 2017 typology of VR locomotion, which analysed 73 instances of 11 techniques in studies published between 2014 and 2017, walking-in-place belongs to the motion-based type: physical interaction that produces continuous motion, in contrast to controller-based joystick movement and to non-continuous teleportation.[28]

The technique's main advantage is that leg movement supplies proprioceptive cues that match the visual motion, which the 1995 and 1999 studies linked to higher presence than flying, and which lets a user cover unlimited virtual distance inside a small room.[1][3] Its costs are physical effort, the latency and detection errors described above, and the fact that the vestibular signal of real forward motion is still missing: the user sees the world move while the inner ear reports bobbing on the spot. Results on sickness are mixed. In the 2026 comparison by Rogers, Ryder and Palmisano (63 recruited, 45 analysed), participants tried teleportation, slide-walk (thumbstick) and walk-in-place in the exploration app Nature Treks VR and the action game Ancient Dungeon VR; teleportation and slide-walk were both rated more usable than walk-in-place, teleportation produced the least nausea and oculomotor discomfort, and walk-in-place, as implemented on a Kat Walk Mini S treadmill, produced the most in both games. Teleportation was strongly preferred for the exploration app, while slide-walk was preferred for immersion in the action game.[11] A 2025 maze study with 15 participants compared Cybershoes, standard controllers and hand-tracked teleportation: Cybershoes matched controller completion times (52 ± 22 s versus 52 ± 25 s on the simplest maze, against 127 ± 54 s for teleportation) and slightly beat controllers on the hardest mazes, but scored lower on the System Usability Scale (67.83 versus 74.67) and produced the highest cybersickness rating (2.9 ± 1.2, against 1.8 ± 0.9 for teleportation and 2.3 ± 1.1 for controllers).[29]

Against real walking, the 1999 study found real walking easier and at least as presence-inducing.[3] Compared with redirected walking, Domna Banakou and Mel Slater's 2023 study on Meta Quest headsets (47 participants: 21 walking in place, 26 using an "interactive redirected walking" method that rotates the world at the boundary) found that the real-walking method gave a stronger sense of being there in small spaces, while walking in place held up better as the available physical space grew.[30] Tregillus and Folmer's 18-user study found no significant difference in performance or reliability between smartphone-based VR-STEP and an auto-walk method, but participants rated VR-STEP more immersive and intuitive.[13]

See also

References

  1. ↑ 1.0 1.1 1.2 1.3 1.4 1.5 Slater, Mel; Usoh, Martin; Steed, Anthony (1995-09). "Taking steps: the influence of a walking technique on presence in virtual reality". ACM Transactions on Computer-Human Interaction, vol. 2, no. 3. pp. 201-219. doi:10.1145/210079.210084. https://dl.acm.org/doi/10.1145/210079.210084.
  2. ↑ 2.00 2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09 2.10 Feasel, Jeff; Whitton, Mary C.; Wendt, Jeremy D. (2008). "LLCM-WIP: Low-Latency, Continuous-Motion Walking-in-Place". 2008 IEEE Symposium on 3D User Interfaces (3DUI). pp. 97-104. doi:10.1109/3DUI.2008.4476598. https://www.cs.unc.edu/~whitton/ExtendedCV/Papers/2008-3DUI-Feasel.pdf.
  3. ↑ 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 Usoh, Martin; Arthur, Kevin; Whitton, Mary C.; Bastos, Rui; Steed, Anthony; Slater, Mel; Brooks, Frederick P. (1999). "Walking > Walking-in-Place > Flying, in Virtual Environments". SIGGRAPH 99: Proceedings of the 26th annual conference on Computer graphics and interactive techniques. pp. 359-364. doi:10.1145/311535.311589. https://www.cise.ufl.edu/research/lok/teaching/ve-s07/papers/1999-SIGGRAPH-Usoh.pdf.
  4. ↑ 4.0 4.1 4.2 4.3 4.4 Templeman, James N.; Denbrook, Patricia S.; Sibert, Linda E. (1999-12). "Virtual Locomotion: Walking in Place through Virtual Environments". Presence: Teleoperators and Virtual Environments, vol. 8, no. 6. pp. 598-617. doi:10.1162/105474699566512. https://direct.mit.edu/pvar/article-abstract/8/6/598/18262/Virtual-Locomotion-Walking-in-Place-through.
  5. ↑ 5.0 5.1 5.2 5.3 5.4 Wendt, Jeremy D.; Whitton, Mary C.; Brooks, Frederick P. (2010). "GUD WIP: Gait-Understanding-Driven Walking-In-Place". Proceedings of the 2010 IEEE Virtual Reality Conference. pp. 51-58. doi:10.1109/VR.2010.5444812. https://pmc.ncbi.nlm.nih.gov/articles/PMC4303911.
  6. ↑ 6.0 6.1 6.2 6.3 6.4 Ryan Sullivan (2016-04-17). "RIPMotion". smirkingcat. http://smirkingcat.software/ripmotion/. Retrieved 2026-09-20.
  7. ↑ 7.0 7.1 7.2 7.3 Charles Singletary (2016-12-21). "PocketStrafe Is A Different Answer To VR Locomotion Issues". UploadVR. https://www.uploadvr.com/pocketstrafe-vr-locomotion-sickness/. Retrieved 2026-09-20.
  8. ↑ 8.0 8.1 8.2 8.3 "Natural Locomotion on Steam". Steam. Valve. https://store.steampowered.com/app/798810/Natural_Locomotion/. Retrieved 2026-09-20.
  9. ↑ 9.0 9.1 9.2 Scott Hayden (2018-10-03). "VR Locomotion Device 'Cybershoes' Kickstarter Triples Funding Goal in First 24 Hours". Road to VR. https://www.roadtovr.com/cybershoes-aims-offer-lower-cost-alternative-vr-treadmills-kickstarter-now-live/. Retrieved 2026-09-20.
  10. ↑ 10.0 10.1 10.2 10.3 "VRocker on Steam". Steam. Valve. https://store.steampowered.com/app/1143750/VRocker/. Retrieved 2026-09-20.
  11. ↑ 11.0 11.1 Rogers, Shane L.; Ryder, Brett; Palmisano, Stephen (2026-08-25). "Comparing User Experiences of Teleportation, Slide-Walk, and Walk-in-Place Locomotion Across Two Virtual Reality Games". Applied Sciences, vol. 16, no. 17. pp. 8472. doi:10.3390/app16178472. https://www.mdpi.com/2076-3417/16/17/8472.
  12. ↑ 12.0 12.1 12.2 Terziman, Léo; Marchal, Maud; Emily, Mathieu; Multon, Franck; Arnaldi, Bruno; Lécuyer, Anatole (2010). "Shake-your-head: revisiting walking-in-place for desktop virtual reality". Proceedings of the 17th ACM Symposium on Virtual Reality Software and Technology (VRST 2010). pp. 27-34. doi:10.1145/1889863.1889867. https://inria.hal.science/hal-00641360.
  13. ↑ 13.0 13.1 13.2 Tregillus, Sam; Folmer, Eelke (2016). "VR-STEP: Walking-in-Place using Inertial Sensing for Hands Free Navigation in Mobile VR Environments". Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. pp. 1250-1255. doi:10.1145/2858036.2858084. https://www.semanticscholar.org/paper/103f982d2a3a9b7335b76d8c7112d1b6fcf0f2bb.
  14. ↑ Tan, Chek Tien; Foo, Leon Cewei; Yeo, Adriel; Lee, Jeannie Su Ann; Wan, Edmund; Kok, Xiao-Feng Kenan; Rajendran, Megani (2022). "Understanding User Experiences Across VR Walking-in-place Locomotion Methods". CHI Conference on Human Factors in Computing Systems (CHI '22). pp. Article 517, 1-13. doi:10.1145/3491102.3501975. https://doi.org/10.1145/3491102.3501975.
  15. ↑ "immersification-wip-locomotion: Unity scripts for a research paper on WIP methods". GitHub. Singapore Institute of Technology. https://github.com/singaporetech/immersification-wip-locomotion. Retrieved 2026-09-20.
  16. ↑ Nilsson, Niels Christian; Serafin, Stefania; Nordahl, Rolf (2014-04). "Establishing the Range of Perceptually Natural Visual Walking Speeds for Virtual Walking-In-Place Locomotion". IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 4. pp. 569-578. doi:10.1109/TVCG.2014.21. https://ieeexplore.ieee.org/document/6777426.
  17. ↑ Yutaro Hirao, Takuji Narumi, Ferran Argelaguet, Anatole Lécuyer (2022-05-10). "Revisiting Walking-in-Place by Introducing Step-Height Control, Elastic Input, and Pseudo-Haptic Feedback". arXiv. https://arxiv.org/abs/2205.04845. Retrieved 2026-09-20.
  18. ↑ Zielasko, Daniel; Bruder, Gerd; Domes, Gregor; Skarbez, Richard; Whitton, Mary C.; Steed, Anthony (2024). "Walking > Walking-in-Place > Flying/Steering > Teleportation? Designing Locomotion Research for Replication and Extension". Proceedings of the 30th ACM Symposium on Virtual Reality Software and Technology (VRST 2024). doi:10.1145/3641825.3689500. https://doi.org/10.1145/3641825.3689500.
  19. ↑ Nilsson, Niels Christian; Serafin, Stefania; Nordahl, Rolf (2016). "Walking in Place Through Virtual Worlds". Human-Computer Interaction. Interaction Platforms and Techniques (HCI International 2016), Lecture Notes in Computer Science vol. 9732. pp. 37-48. doi:10.1007/978-3-319-39516-6_4. https://vbn.aau.dk/en/publications/walking-in-place-through-virtual-worlds/.
  20. ↑ David Jagneaux (2016-07-18). "Developers Test Running in Place To Solve VR's Movement Problem". UploadVR. https://www.uploadvr.com/running-in-place-movement-locomotion-vr-developers/. Retrieved 2026-09-20.
  21. ↑ Myou (2018-12-24). "Feet tracker support RELEASED in beta! Added 15 new profiles and many fixes.". Steam Community. Valve. https://steamcommunity.com/games/798810/announcements/detail/2417769111298654838. Retrieved 2026-09-20.
  22. ↑ Myou (2019-06-28). "Valve Index, Rift S, Walk in place with smartphones and much more!". Steam Community. Valve. https://steamcommunity.com/games/798810/announcements/detail/2436926440568923580. Retrieved 2026-09-20.
  23. ↑ Myou (2020-10-13). "Natural Locomotion is compatible with Oculus Quest 2". Steam Community. Valve. https://steamcommunity.com/games/798810/announcements/detail/3853351107567595771. Retrieved 2026-09-20.
  24. ↑ 24.0 24.1 digitalsoulVR (2026-08-26). "Update Notes For 25/08/2026". Steam Community. Valve. https://steamcommunity.com/games/1143750/announcements/detail/1842212951298285. Retrieved 2026-09-20.
  25. ↑ 25.0 25.1 25.2 Scott Hayden (2025-05-16). "VR's Quirkiest Locomotion Device 'Cybershoes' Shuts Its Doors for Good". Road to VR. https://roadtovr.com/vrs-quirkiest-locomotion-device-cybershoes-shuts-down/. Retrieved 2026-09-20.
  26. ↑ Tomislav Bezmalinovic (2025-05-14). "The VR startup Cybershoes has shut down". MIXED. https://mixed-news.com/en/cybershoes-shutdown/. Retrieved 2026-09-20.
  27. ↑ "Cybershoes - Shoes made for walking in VR". Cybershoes. https://www.cybershoes.com/. Retrieved 2026-09-20.
  28. ↑ Boletsis, Costas (2017-09-28). "The New Era of Virtual Reality Locomotion: A Systematic Literature Review of Techniques and a Proposed Typology". Multimodal Technologies and Interaction, vol. 1, no. 4. pp. 24. doi:10.3390/mti1040024. https://www.mdpi.com/2414-4088/1/4/24.
  29. ↑ Hořejší, Petr; Lochmannová, Alena; Jezl, Vojtěch; Dvořák, Matěj (2025-07-19). "Virtual reality locomotion methods differentially affect spatial orientation and cybersickness during maze navigation". Scientific Reports, vol. 15. pp. 26255. doi:10.1038/s41598-025-12143-y. https://www.nature.com/articles/s41598-025-12143-y.
  30. ↑ Banakou, Domna; Slater, Mel (2023-11-20). "A comparison of two methods for moving through a virtual environment: walking in place and interactive redirected walking". Frontiers in Virtual Reality, vol. 4. pp. 1294539. doi:10.3389/frvir.2023.1294539. https://www.frontiersin.org/journals/virtual-reality/articles/10.3389/frvir.2023.1294539/full.
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