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Record W2060075501 · doi:10.1109/isspa.2012.6310660

Design of a neuromuscular disorders diagnostic system using human movement analysis

2012· article· en· W2060075501 on OpenAlexaff
Christian O’Reilly, Réjean Plamondon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsKinematicsPhysical medicine and rehabilitationComputer scienceMovement disordersReceiver operating characteristicStroke (engine)Diabetes mellitusRisk analysis (engineering)MedicineSimulationEngineeringMachine learningMechanical engineeringPathology

Abstract

fetched live from OpenAlex

This communication summarizes the outcome of our research program on the design of a diagnostic system for neuromuscular disorders based on the analysis of human movement using the Kinematic Theory of Rapid Human Movements. Herein, this design problem is split in sub-problems which are then described. The solutions adopted at each design step are explained. As an example of application, typical results obtained so far for the assessment of the most important modifiable risk factors of brain stroke (diabetes, hypertension, hypercholesterolemia, obesity, cardiac problems, and cigarette smoking) are reported by the means of the area under the receiver operating characteristic curve (AUC).

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 score0.498
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.223
Teacher spread0.205 · 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

Citations33
Published2012
Admission routes1
Has abstractyes

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