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Record W2312443021 · doi:10.11159/vwhci.2013.002

Developing Wii Balance Games to Increase Balance: A Multi-Disciplinary Approach

2013· article· en· W2312443021 on OpenAlexvenueno aff
T. Claire Davies, Mark Deacon, Jotinder Singh, Zachary Holly, Lynne S. Taylor, Sean Mathieson, John Parsons

Bibliographic record

VenueInternational Journal of Virtual Worlds and Human Computer Interaction · 2013
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsBalance (ability)DisciplineSociologyPhysical medicine and rehabilitationMedicineSocial science

Abstract

fetched live from OpenAlex

Persons older than 75 years of age are more susceptible to falls and are a greater burden to the healthcare system than their younger counterparts. While devices like the Nintendo Wii can potentially increase balance control, virtual reality games available for the Wii are not designed as exercise therapy programmes for older people. Targeted therapies are more likely to increase function and decrease falls risk. An interdisciplinary approach that integrates the knowledge of engineers with clinicians can enable the development of a virtual reality home-based exercise programme with off-theshelf equipment. Two targeted exercise therapy games have been developed to encourage at home exercise using novel software with Wii balance boards. One requires stability and balance in a standing position, while the other trains stepping reactions. Ethics approval was obtained to evaluate and pilot these games with older participants. These games were shown to be effective during user testing in that five individuals have provided feedback for further improvement and are interested in participating in a longer balance and exercise programme using these games. One participant was also involved in testing the device in his home environment over the course of a few days. Initial testing in the home environment exposed issues that were not present in the laboratory environment which supports the need for user testing at a very early stage in the prototype development.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.595

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 communication0.0000.001
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 head0.044
GPT teacher head0.376
Teacher spread0.332 · 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 designObservational
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

Citations2
Published2013
Admission routes1
Has abstractyes

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