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Record W2081739120 · doi:10.1515/icom.2013.0012

Motion-Based Game Interaction for Older Adults / Bewegungsbasierte Spielinteraktion für Senioren

2013· article· de· W2081739120 on OpenAlexafffund
Kathrin Gerling

Bibliographic record

Venuei-com · 2013
Typearticle
Languagede
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHumanitiesPolitical scienceGynecologyArtMedicine

Abstract

fetched live from OpenAlex

Zusammenfassung Bewegungsbasierte Videospiele für Senioren erfreuen sich immer größerer Beliebtheit, da sie eine geeignete Möglichkeit darstellen, ältere Menschen zu körperlicher Aktivität zu motivieren. In diesem Zusammenhang ist es wichtig zu gewährleisten, dass bewegungsbasierte Eingabegeräte für entsprechende Spiele leicht zugänglich sind, und dass Eingabeparadigmen kein Verletzungsrisiko für ältere Nutzer darstellen. Dieser Artikel gibt einen Überblick über traditionelle und bewegungsbasierte Spielinteraktion für Senioren, und zeigt Möglichkeiten auf, wie es gebrechlichen älteren Menschen ermöglicht werden kann, ihren Körper in den Interaktionsprozess einzubringen. Weiterhin fasst dieser Artikel potentielle Anwendungsfälle für bewegungsbasierte Spielinteraktion für Senioren zusammen.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0150.002

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 head0.012
GPT teacher head0.289
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2013
Admission routes2
Has abstractyes

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