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Record W2545637989 · doi:10.1109/gem.2014.7048101

Powered to play: A mixed reality game for people driving powered chairs

2014· article· en· W2545637989 on OpenAlexafffund
Jamal K. Edey, Katie Seaborn, Carmen Branje, Deborah I. Fels

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of TorontoToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUSableComputer scienceMixed realityMultimediaHuman–computer interactionEntertainmentAugmented realityMobile phonePhoneMobile deviceGlobal Positioning SystemTelecommunicationsWorld Wide Web

Abstract

fetched live from OpenAlex

People who drive powered chairs such as scooters and wheelchairs are often excluded from physical play activities because of their mobility differences. Video games that capture body motion such as sports or dancing games also often exclude people with limited mobility. However, combining the advantages of digital environments such as flexible virtualized scenes and controls with the physical advantages of having a powered chair may offer powered mobility device users social entertainment opportunities that are not otherwise available. Powered to Play is developed as a mixed reality capture-the-flag game for people who drive powered mobility devices. It uses a GPS-enabled mobile phone to display and interact with users in combination with the physical environment. Evaluation of the game was carried out with thirteen mobility devices users during two gameplay sessions located in different physical settings. Our findings show that a mixed reality game enabled by mobile phone interfaces in a large outdoor area was usable, feasible, and generally well-received by users.

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)0.003
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: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0010.000
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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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

Citations18
Published2014
Admission routes2
Has abstractyes

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