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Record W2151515118 · doi:10.1109/iembs.2005.1616935

Development of an Interactive Motivating Tool for Rehabilitation Movements

2005· article· en· W2151515118 on OpenAlexaff
Aimee L. Betker, Tony Szturm, Zahra Moussavi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBiofeedbackHuman–computer interactionComputer sciencePhysical medicine and rehabilitationBalance (ability)Center of pressure (fluid mechanics)RehabilitationBalance disordersBalance trainingTrajectoryPopulationSimulationPhysical therapyMedicineEngineering

Abstract

fetched live from OpenAlex

In this paper, an interactive tool, including three computer games controlled via the center of foot pressure (COP) trajectory biofeedback, was designed to aid in pressure balance for rehabilitating persons with balance disorders. The games interact in real-time with the Vista Medical Force Sensitive Applications software and pressure mat. The main goal of this research was to employ attractive and motivational learning techniques, using equipment that is available to a large population, to increase volume of exercise practice and to retain the patient's attention. Questionnaires regarding the motivational aspects of the games were administered to 15 subjects (7 patients). The results indicate that the tools were indeed attractive, motivational and an improvement to conventional exercise regimes.

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)0.003
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.030
GPT teacher head0.398
Teacher spread0.367 · 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

Citations24
Published2005
Admission routes1
Has abstractyes

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