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Design and Preliminary Implementation of an Air Microfluidics Enabled Soft Robotic Knee Brace Towards the Management of Osteoarthritis

2020· article· en· W3082888832 on OpenAlexaff
Run Ze Gao, Kendal A. Marriott, Clark R. Dickerson, Monica R. Maly, Carolyn L. Ren

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBraceSoft roboticsWearable computerRoboticsEngineeringMicrofluidicsRobotComputer scienceRehabilitation roboticsSimulationArtificial intelligenceMechanical engineeringEmbedded systemNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

A dynamic and low-profile unloader tibiofemoral knee brace is designed and prototyped by synergizing concepts from the fields of microfluidics and soft robotics. Microfluidics provides strategies for miniaturization and multiplexing while soft robotics afford the tools to create soft fluidic actuators and allow compliant and inherently safe robotic assistance as part of clothing. The unloader knee brace provides dynamic response during the gait cycle, where a three-point leverage torque is provided only during the stance phase to contribute to joint stability when required and enhance comfort and compliance.Clinical Relevance- This novel soft robotic brace has the potential to reduce device abandonment due to aesthetics, user non-compliance and discomfort due to a constant three-point leverage torque during the gait cycle. Also, this air microfluidics enabled soft robotic knee brace could be expanded upon to improve the efficacy of braces in general and augment the effects of physical therapy, rehabilitation and treatment of musculoskeletal conditions.

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.

How this classification was reachedexpand

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.590
Threshold uncertainty score0.273

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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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