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A Stochastic Model of Knee Angle in Response to Electrical Stimulation of the Quadriceps and Hamstrings Muscles

2011· article· en· W1950991710 on OpenAlexaff
Cheryl L. Lynch, Miloš R. Popović

Bibliographic record

VenueArtificial Organs · 2011
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of TorontoToronto Rehabilitation Institute
Fundersnot available
KeywordsFunctional electrical stimulationKnee flexionStimulationKnee JointQuadriceps muscleStochastic modellingStandard deviationPhysical medicine and rehabilitationControl theory (sociology)Computer scienceSimulationMathematicsMedicineStatisticsSurgery

Abstract

fetched live from OpenAlex

A novel stochastic model of knee angle in response to stimulation of the quadriceps and hamstrings muscle groups is presented. This model includes uncertainty due to fatigue and day-to-day changes in the stimulated muscles. The model consists of a normally distributed random variable whose mean and standard deviation vary with time and is characterized using data from a complete spinal cord injuries subject. The experimental data show a significant difference between the left and right legs under certain conditions, and suggest that fatigue-related and day-to-day variation may also be important. The purpose of this model is to generate more realistic electrically stimulated knee movements. This stochastic modeling technique could be incorporated into a comprehensive model of a joint actuated with electrical stimulation, and has great potential as a tool for analyzing closed-loop performance of electrically stimulated systems.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.214
Teacher spread0.188 · 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 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

Citations10
Published2011
Admission routes1
Has abstractyes

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