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Record W2024630350 · doi:10.1109/titb.2012.2206116

3-D Streaming Supplying Partner Protocols for Mobile Collaborative Exergaming for Health

2012· article· en· W2024630350 on OpenAlex

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

VenueIEEE Transactions on Information Technology in Biomedicine · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicMultimedia Communication and Technology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsLonelinessPhobiasMental healthPhysical activityAnxietyBottleneckObesityInternet privacyComputer scienceMultimediaPsychologyMedicinePhysical therapySocial psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Childhood obesity is nowadays considered as one of the major health problems that many societies suffer from. The obesity epidemic leads to several life threatening conditions such as diabetes, heart disease, high blood pressure, and mental health problems like depression, anxiety and loneliness just to mention a few. Several approaches, including physical exercises, strict dietary, and exergames among others, have been adopted to address the obesity epidemic. Exergames are considered the innovative approach for fighting several health problem such as the obesity, where a combination of exercise and 3D gaming are proposed to incite kids to exercise as a team. Collaborative exergaming became even more popular given that it addresses the social side of the obesity epidemic, and it motivates kids to socialize with other kids. Traditional exergames are based on the client server approach where the server is responsible for streaming the 3D environment. However, this can lead to latency and server bottleneck if many clients participate in the exergame, which leads to the kids stopping exercising. Having an exergame application that does not suffer from networking problem such as delay, is very important given that it increases the exercise hours. In this work, we propose a new trend of mobile collaborative exergming applications that is based on the peer-to-peer (P2P) architecture, as well as two supplying partner selection protocols that aim at selecting the suitable source responsible for streaming the relevant 3D data. Our system, that we refer to as MOSAIC, is intended for mobile collaborative exergames that incite kids to move inside a large area, using thin mobile devices such as head mounted devices (HMD), have physical exercises, and collaborate with other kids which in consequence address several health problems such as the obesity epidemic on the physical and social plans. Our proposed mobile collaborative exergame aims at inciting the kids to exercise as a team for a longer time by improving the quality of the streaming and reducing the delay. This is accomplished by our proposed supplying partner selection protocols that provide a quick discovery of multiple supplying partners, by minimizing the time required the to acquire data. The performance evaluation we have obtained to evaluate our suite of protocols using a realistic set of exergame scenarios for obese kids is then presented and discussed.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.033
GPT teacher head0.405
Teacher spread0.372 · 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