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Record W2328494651 · doi:10.11159/vwhci.2015.001

Examining Dynamic Control-Display Gain Adjustments to Assist Mouse-Based Pointing for Youths with Cerebral Palsy

2015· article· en· W2328494651 on OpenAlexvenueno aff
Amur Al Manji, Claire Davies, Robert Amor

Bibliographic record

VenueInternational Journal of Virtual Worlds and Human Computer Interaction · 2015
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsCerebral palsyPhysical medicine and rehabilitationControl (management)Computer sciencePsychologyAudiologyMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Youths with cerebral palsy encounter difficulties in target acquisition using mouse-based "point-and-click" tasks. The performance of "point-and-click computer tasks" by youths with cerebral palsy still needs improvement to allow easy target acquisition. The purpose of this study was to evaluate an algorithm intended to enhance the performance of point-andclick computer tasks for youths with cerebral palsy using a standard mouse. The curvature index-based algorithm for dynamic adjustment of control-display gain showed experimentally better performance during primary submovement, but worse performance during secondary correction sub-movements (higher number of sub-movements and longer movement time) for both typically developed youths and youths with cerebral palsy. It also showed better performance of average speed and maximum speed compared with Windows default settings. Furthermore, the average movement time, error rate, and overshoot rate for both typically developed youths and youths with cerebral palsy are higher using the curvature index-based algorithm.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.329
Teacher spread0.271 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations5
Published2015
Admission routes1
Has abstractyes

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