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Record W2102141110 · doi:10.1109/iembs.2007.4353404

Occupational Therapists' Evaluation of Haptic Motor Rehabilitation

2007· article· en· W2102141110 on OpenAlexaffabout
Ruba Kayyali, Atif Alamri, Mohamad Eid, Rosa Iglesias, Shervin Shirmohammadi, Abdulmotaleb El Saddik, Edward D. Lemaire

Bibliographic record

VenueConference proceedings · 2007
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Ottawa
Fundersnot available
KeywordsHaptic technologyRehabilitationVirtual realityComputer scienceHuman–computer interactionGraphical user interfacePhysical medicine and rehabilitationVirtual machineSimulationPhysical therapyMedicineOperating system

Abstract

fetched live from OpenAlex

Haptic-based virtual rehabilitation systems have recently become a subject of interest. In addition to the benefits provided by virtual rehabilitation, the haptic-based systems offer force and tactile feedback which can be for upper and lower extremity rehabilitation. In this paper, we present a system that uses haptics, in conjunction with virtual environments, to provide a rich media environment for motor rehabilitation of stroke patients. The system also provides Occupational Therapists (OTs) with a Graphical User Interface (GUI) that enables them to configure the hardware and virtual exercises and to monitor patients' performance. We also present an analysis of the system by a group of OTs from the Ottawa General Hospital, Rehabilitation Center. The OT's feedback, both the positives and negatives, and the results of the assessment test are also presented.

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)0.010
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.078
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.046
GPT teacher head0.346
Teacher spread0.300 · 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 designQualitative
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

Citations15
Published2007
Admission routes2
Has abstractyes

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