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Record W2003153253 · doi:10.3109/17483101003719037

A technique to train finger coordination and independence after stroke

2010· article· en· W2003153253 on OpenAlexafffund
Ludovic Dovat, Olivier Lambercy, Berna Salman, Vineet Johnson, Theodore E. Milner, Roger Gassert, Etienne Burdet, Teo Chee Leong

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2010
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMcGill UniversitySimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaImperial College LondonNational University of SingaporeMcMaster University
KeywordsIndependence (probability theory)RehabilitationPhysical medicine and rehabilitationStroke (engine)Closing (real estate)Computer scienceChronic strokeSimulationArtificial intelligencePhysical therapyPsychologyMedicineEngineeringMathematicsPolitical scienceStatisticsMechanical engineering

Abstract

fetched live from OpenAlex

PURPOSE: Finger coordination and independence are often impaired in stroke survivors, preventing them from performing activities of daily living. We have developed a technique using a robotic interface, the HandCARE, to train these functions. METHOD: The Hand Cable-Actuated REhabilitation (CARE) system can assist the subject in opening and closing movements of the hand, and can be adapted to accommodate various hand sizes and finger shapes. RESULTS: Two game-like exercises have been developed, which use a motivating approach to promote recovery of finger coordination and independence. Mathematical measures have been implemented to evaluate these functions. This technique is validated with two post-stroke subjects who practiced for 20 minutes twice a week during eight weeks. CONCLUSIONS: The results show significant improvements in finger coordination, force modulation as well as finger independence.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.276
Teacher spread0.270 · 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

Citations18
Published2010
Admission routes2
Has abstractyes

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