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Record W3022706795 · doi:10.1002/aisy.202000071

Robotics, Smart Wearable Technologies, and Autonomous Intelligent Systems for Healthcare During the COVID‐19 Pandemic: An Analysis of the State of the Art and Future Vision

2020· article· en· W3022706795 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced Intelligent Systems · 2020
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchMinistry of Economic Development and Trade, Government of Alberta
KeywordsCoronavirus disease 2019 (COVID-19)PandemicRoboticsWearable computer2019-20 coronavirus outbreakArtificial intelligenceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Health careState (computer science)Computer scienceState of artWearable technologyHuman–computer interactionRobotData scienceMedicineEmbedded systemVirologyInfectious disease (medical specialty)Political science

Abstract

fetched live from OpenAlex

Herein, how robotic and autonomous systems and smart wearables complement and support healthcare delivery and the healthcare staff during the COVID‐19 pandemic are presented. For instance, robotic and telerobotic systems significantly reduce the risk of infectious disease transmission to frontline healthcare workers by making it possible to triage, evaluate, monitor, and treat patients from a safe distance. Various examples of where the medical, engineering, and science communities come together to aid the healthcare system, healthcare workers, and society during the current crisis are presented. The goal is to encourage an interdisciplinary dialog so that ethical, practical, and beneficial technological solutions are found to effectively tackle this and similar crises.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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