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Record W3122513307 · doi:10.1002/admt.202000938

3D Origami Sensing Robots for Cooperative Healthcare Monitoring

2021· article· en· W3122513307 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 Materials Technologies · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRobotHumanoid robotAdaptabilityHuman–computer interactionComputer scienceGaitHealth careArtificial intelligenceSimulationEngineeringPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Abstract In this study, cooperative healthcare sensing robots that closely monitor and evaluate the patients’ muscle functions through gait analysis and electromyography (EMG) are developed. By integrating the biological sensors, the sensing robot can recognize the vital signs. The sensing robots are developed by the design and optimization of their architectures and materials using a green strategy. To achieve mechanically durable robot designs, 3D origami structures are used with specific optimum criteria. Different sensing robot applications are created through the 3D origami insole and humanoid hands for healthcare monitoring. The smart insole built with 3D origami monitors the foot pressure distribution for gait analysis of patients, and the humanoid hand equipped with the 3D origami‐structured EMG fingers cooperatively detects EMG signals. Such cooperative sensing robots hold considerable promise for healthcare monitoring with convenience for patients with quality of care, because the robots can derive empathetic adaptability with humans.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.022
GPT teacher head0.270
Teacher spread0.248 · 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