MétaCan
Menu
Back to cohort
Record W1492483197 · doi:10.1155/2015/590584

A Novel Study on Natural Robotic Rehabilitation Exergames Using the Unaffected Arm of Stroke Patients

2015· article· en· W1492483197 on OpenAlexaff
Mohamad Hoda, Yehya Hoda, Atif Alamri, Basim Hafidh, Abdulmotaleb El Saddik

Bibliographic record

VenueInternational Journal of Distributed Sensor Networks · 2015
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRehabilitationSession (web analytics)Computer scienceNoveltyRobotic armPhysical medicine and rehabilitationVirtual realitySimulationWired gloveHuman–computer interactionPhysical therapyMedicineArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

We design and implement a low-cost rehabilitation glove to meet the needs of those patients who have paralysis in their affected hand. The novelty of this glove is that it is to be worn on the unaffected hand which acts as a natural robotic arm during the rehabilitation session. The glove is equipped with FSR sensors that measure the forces exerted by the affected hand on the unaffected hand. A virtual reality rehabilitation game is developed using Microsoft Kinect to facilitate the exercises and motivate the patients. The system is tested on three patients for six weeks. Objective measurements showed that patients have significantly improved over the study period. Moreover, the patients themselves gave positive feedback on the whole system; wearing the glove on the unaffected hand made their life easier and let them enjoy the rehabilitation sessions.

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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.023
GPT teacher head0.306
Teacher spread0.283 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Distributed Sensor NetworksSame topicStroke Rehabilitation and RecoveryFrench-language works237,207