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Record W4214658756 · doi:10.1177/20556683211067174

Comparing the usability of a virtual reality manual wheelchair simulator in two display conditions

2022· article· en· W4214658756 on OpenAlexafffund
Mrityunjaya A Alapakkam Govindarajan, Philippe S. Archambault, Youri Laplante-El Haili

Bibliographic record

VenueJournal of Rehabilitation and Assistive Technologies Engineering · 2022
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsMcGill UniversityJewish Rehabilitation Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUsabilityWheelchairVirtual realityComputer scienceSimulationHuman–computer interactionComputer graphics (images)World Wide Web

Abstract

fetched live from OpenAlex

Virtual reality (VR) simulators can help train manual wheelchair skills. Transfer of skills from the virtual to the real world may depend on the sense of presence, or of being "in" the virtual environment. OBJECTIVES: To compare 1) the usability (in terms of performance, overall experience, and satisfaction), as well as 2) the sense of presence, in a wheelchair simulator with two display conditions: a head-mounted display (HMD) or a computer monitor. METHODS: Sixteen healthy adults practiced in the wheelchair simulator, first with a computer monitor display and then with an HMD. Task performance, cybersickness, presence, and overall experience in VR were assessed. RESULTS: Four of the participants were unable to complete all tasks in the HMD condition. When comparing the two display conditions, performance was the same, except for one task (bathroom) which took longer with the computer monitor. The HMD condition was rated as significantly higher in terms of sense of presence and VR experience but provoked more intense symptoms of cybersickness. DISCUSSION: Use of an HMD increased symptoms of cybersickness, with small gains in wheelchair performance. Thus, the use of an HMD may be warranted for the training of wheelchair skills, if tolerated by participants.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.298
Teacher spread0.281 · 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 designSimulation or modeling
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

Citations13
Published2022
Admission routes2
Has abstractyes

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