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Record W2146737178 · doi:10.1186/1743-0003-4-20

Virtual reality environments for post-stroke arm rehabilitation

2007· article· en· W2146737178 on OpenAlexafffund
Sandeep Subramanian, Luiz Alberto Manfré Knaut, Christian Beaudoin, Bradford J. McFadyen, Anatol G. Feldman, Mindy F. Levin

Bibliographic record

VenueJournal of NeuroEngineering and Rehabilitation · 2007
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversité LavalUniversité de MontréalMcGill UniversityJewish Rehabilitation Hospital
FundersCanadian Institutes of Health Research
KeywordsVirtual realityPhysical medicine and rehabilitationHuman–computer interactionComputer scienceRehabilitationSalientElevatorMotor learningVirtual machinePsychologyMedicineArtificial intelligenceNeuroscienceEngineering

Abstract

fetched live from OpenAlex

INTRODUCTION: Optimal practice and feedback elements are essential requirements for maximal motor recovery in patients with motor deficits due to central nervous system lesions. METHODS: A virtual environment (VE) was created that incorporates practice and feedback elements necessary for maximal motor recovery. It permits varied and challenging practice in a motivating environment that provides salient feedback. RESULTS: The VE gives the user knowledge of results feedback about motor behavior and knowledge of performance feedback about the quality of pointing movements made in a virtual elevator. Movement distances are related to length of body segments. CONCLUSION: We describe an immersive and interactive experimental protocol developed in a virtual reality environment using the CAREN system. The VE can be used as a training environment for the upper limb in patients with motor impairments.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.258
Teacher spread0.250 · 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

Citations131
Published2007
Admission routes2
Has abstractyes

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