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Record W2097415926 · doi:10.1109/tnsre.2008.2010347

HandCARE: A Cable-Actuated Rehabilitation System to Train Hand Function After Stroke

2008· article· en· W2097415926 on OpenAlexaff
Ludovic Dovat, Olivier Lambercy, Roger Gassert, Thomas Maeder, Teo Chee Leong, Etienne Burdet

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2008
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMcGill University
Fundersnot available
KeywordsInterface (matter)Closing (real estate)ActuatorClutchSimulationComputer scienceFunction (biology)Finger tappingEngineeringMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

We have developed a robotic interface to train hand and finger function. HandCARE is a Cable-Actuated REhabilitation system, in which each finger is attached to an instrumented cable loop allowing force control and a predominantly linear displacement. The device, whose designed is based on biomechanical measurements, can assist the subject in opening and closing movements and can be adapted to accommodate various hand shapes and finger sizes. Main features of the interface include a differential sensing system, and a clutch system which allows independent movement of the five fingers with only one actuator. The device is safe, easily transportable, and offers multiple training possibilities. This paper presents the biomechanical measurements carried out to determine the requirements for a finger rehabilitation device, and the design and characterization of the complete system.

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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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

Citations230
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Neural Systems and Rehabilitation EngineeringSame topicStroke Rehabilitation and RecoveryFrench-language works237,207