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Record W2046136296 · doi:10.1145/2591357.2591361

Motion-based game interaction for older adults

2014· article· en· W2046136296 on OpenAlex
Kathrin Gerling

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM SIGACCESS Accessibility and Computing · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsContext (archaeology)Motion (physics)Life expectancyPsychologyVariety (cybernetics)CognitionVideo gameApplied psychologyQuality of life (healthcare)MultimediaGerontologyComputer scienceMedicinePopulation

Abstract

fetched live from OpenAlex

Decreased activity reduces life expectancy, yet many institutionalized older adults lead sedentary lifestyles: age-related changes and impairments limit the number accessible leisure activities, and nursing homes struggle to provide mental and physically stimulating activities for their residents. In this context, motion-based video games -- games that integrate physical user input -- are one opportunity of fostering physical activity, and research suggests that these games have a variety of positive effects on the well-being of older adults. However, currently available games are too demanding for this audience. My research will help foster the design of accessible and safe motion-based video games for older adults. In my PhD research, I explore motion-based game interaction design for older adults. By creating enjoyable video games for this audience, my research will help encourage cognitive and physical activity among nursing home residents, thereby increasing their quality of life.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.683

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.334
Teacher spread0.311 · 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