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Record W2117405826

Robotic techniques for upper limb rehabilitation and evaluation

2006· article· en· W2117405826 on OpenAlexaff
Hussein A. Abduallah, Cole Tarry, Mohamed Abderrahim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsRehabilitationProcess (computing)SoftwareWork (physics)Robotic armComputer scienceSimulationEngineeringControl engineeringPhysical medicine and rehabilitationArtificial intelligencePhysical therapyMedicineMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

Abstract:- In this paper we discuss the development of a therapeutic robotic system using an industrial manipulator, off-the-shelf hardware and in-house developed software. The development work starts with the analysis and the establishment of the requirements for a rehabilitation robotic system (RRS) and describes the proposed prototype and its operation. The developed system incorporates the necessary hardware and software modules to permit its use in safe and effective rehabilitation exercises. Tests were conducted on healthy individual and showed a correct behaviour of the system. The developed biomechanical model of the patient’s arm and the rehabilitation exercise collected data simulate the dynamic behaviour of the arm and allow for the extraction of forces at the joint level. This information with other indicators will be used for the evaluation of the recovery process

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.002

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 head0.015
GPT teacher head0.319
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2006
Admission routes1
Has abstractyes

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