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Record W2172231451 · doi:10.1109/icorr.2009.5209520

Rehabilitation of grasping and forearm pronation/supination with the Haptic Knob

2009· article· en· W2172231451 on OpenAlexafffund
Olivier Lambercy, Ludovic Dovat, Hong Yun, Seng Kwee Wee, Christopher Kuah, Karen Sui Geok Chua, Roger Gassert, Theodore E. Milner, Chee Leong Teo, Etienne Burdet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMcGill University
FundersSimon Fraser UniversityNational Science Foundation
KeywordsPhysical medicine and rehabilitationRehabilitationHaptic technologyWristForearmStroke (engine)Upper limbHomogeneousRobotChronic strokePhysical therapyRobot end effectorPsychologyComputer scienceMedicineArtificial intelligenceSurgeryMathematicsEngineering

Abstract

fetched live from OpenAlex

This paper investigates robot-assisted rehabilitation after stroke using the haptic knob, a 2 degree-of-freedom end-effector based robotic device to train grasping and wrist pronation/supination. Nine chronic stroke subjects trained over a period of 6 weeks, with 3 one-hour sessions of robot-assisted therapy per week, consisting of two exercises requiring active participation promoted by therapeutic games. Results of standard clinical assessments demonstrate the positive effects of robot-assisted therapy with the haptic knob. Subjects improved by a mean of 4.3 points in the Fugl-Meyer assessment scale, together with a decrease in hand impairments such as abnormal muscle tone frequently observed in stroke subjects. Significant improvements were also observed in motor function of the upper arm as a result of the robot-assisted therapy, suggesting homogeneous improvement of upper limb function as a result of distal training.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.007
GPT teacher head0.254
Teacher spread0.247 · 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 designObservational
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

Citations38
Published2009
Admission routes2
Has abstractyes

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