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Record W2782170080

Using Artificial Neural Network to Model EMG Signals from the Prime Movers of the Shoulder for Rehabilitation Robotic Systems

2002· article· en· W2782170080 on OpenAlexaff
D. Matheson Rittenhouse, Hussein A. Abdullah, R J Runciman, Otman Basir

Bibliographic record

VenueCMBES Proceedings · 2002
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsArtificial neural networkRehabilitationRobotPrime (order theory)Computer scienceArtificial intelligenceElectromyographyEngineeringPhysical medicine and rehabilitationMedicinePhysical therapyMathematics
DOInot available

Abstract

fetched live from OpenAlex

Artificial neural networks have demonstrated some ability to model the electromyogram (EMG) signals from prime movers of the joint under investigation. This paper demonstrates the ability of a fully connected feed forward neural network (FF NN) to predict EMG signals from eight muscles of the shoulder. Robots used for physical rehabilitation can incorporate the information from EMG as an input to an intelligent decision making algorithm used to adjust the level of difficulty according to patient performance.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.331

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 communication0.0000.000
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 head0.051
GPT teacher head0.250
Teacher spread0.198 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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Same venueCMBES ProceedingsSame topicMuscle activation and electromyography studiesFrench-language works237,207