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A Makerspace Foot Pedal and Shoe Add-On for Seated Virtual Reality Locomotion

2019· article· en· W3001000808 on OpenAlexaff
Marco Valdez Balderas, Christopher L. Carmichael, Bill Ko, Atiya Nova, Angela Tabafunda, Álvaro Uribe-Quevedo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsVirtual realityHuman–computer interactionComputer scienceImmersion (mathematics)UsabilityMetaverseRendering (computer graphics)Wearable computerMultimediaArtificial intelligence

Abstract

fetched live from OpenAlex

Virtual reality advances are constantly pushing the envelope of what is possible in the realm of human-computer interaction to provide more engaging and immersive experiences. Recent trends in consumer virtual reality are seeing the development of stand-alone headsets providing portable wireless experiences. However, one aspect of VR that is still in need of improvement is user-friendly, immersive, intuitive, and effective locomotion. Currently, commodity locomotion is achieved through teleportation, gamepad-based, or room-scale locomotion, unlike high-end systems featuring omnidirectional treadmills that require higher investment and specialized infrastructure. Proper virtual reality locomotion can increase immersion, presence, and allow effective interactions that can positively impact applications in health care, education, training, and safety. In this paper, we present a preliminary usability study on seated virtual reality locomotion employing consumer-level devices, teleportation, and two custom-made devices employing 3D printing and open electronics as an alternative for walking in VR.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.297
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2019
Admission routes1
Has abstractyes

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