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Record W4404658067 · doi:10.1145/3705320

Meta-Review of Wearable Devices for Healthcare in the Metaverse

2024· article· en· W4404658067 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

VenueACM Transactions on Multimedia Computing Communications and Applications · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsArtificial Intelligence in Medicine (Canada)University of Ottawa
Fundersnot available
KeywordsComputer scienceWearable computerMetaverseHuman–computer interactionHealth careWearable technologySmartwatchData scienceWorld Wide WebInternet privacyVirtual reality

Abstract

fetched live from OpenAlex

In recent years, there has been a growing interest in leveraging the metaverse to enhance community engagement and healthcare. This article provides a comprehensive examination of wearable devices and sensors utilized within immersive environments to improve well-being and healthcare outcomes. We categorize the healthcare application domains that employ wearable devices and identify commonly used devices and sensors based on a thorough review of the literature. Our study offers a detailed summary of these applications, highlighting their potential to enhance overall quality of life through remote monitoring, rehabilitation, and chronic disease management. Furthermore, we address existing research gaps and challenges in this field, offering insights for future research directions. This meta-review emphasizes the need for further exploration in the rapidly evolving domain of wearable healthcare technologies within the metaverse, presenting an overview of the current state of wearable devices in healthcare and underscoring their significance in advancing healthcare delivery and outcomes.

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 score0.837
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.122
GPT teacher head0.395
Teacher spread0.273 · 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