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Record W1974009377 · doi:10.17083/ijsg.v1i4.48

Use of Ecological Gestures in Soccer Games Running on Mobile Devices

2014· article· en· W1974009377 on OpenAlex
Valère Plantevin, Bob-Antoine J. Ménélas

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

VenueInternational Journal of Serious Games · 2014
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsGestureExploitComputer scienceHuman–computer interactionContext (archaeology)Mobile deviceWearable computerMultimediaVirtual realityGesture recognitionArtificial intelligenceWorld Wide WebComputer securityEmbedded systemGeography

Abstract

fetched live from OpenAlex

The strong integration of “intelligent mobile devices” into modern societies offers a great potential for a wide spread distribution of mobile serious games. As in the case of Virtual Reality based systems, in order to be useful and efficient, these serious games need to be validated ecologically. In this context, this paper addresses the use of ecological interactions for a mobile serious game. We exploit a wearable insole in order to let users interact with a virtual soccer game via real-world soccer movements. We analyzed the concept of ecological interactions. The system used for recognition of ecological gestures is also detailed. A primary study showed that proposed system can be exploited for real time gesture recognition on a mobile device.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.328

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 science0.0010.000
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 head0.294
Teacher spread0.276 · 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