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Sensitivity of 24-h EMG duration and intensity in the human vastus lateralis muscle to threshold changes

2009· article· en· W2147250502 on OpenAlexaff
Cliff S. Klein, Lillian B. Peterson, SEAN M. FERRELL, Christine K. Thomas

Bibliographic record

VenueJournal of Applied Physiology · 2009
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Toronto
FundersNational Institute of Neurological Disorders and Stroke
KeywordsElectromyographyMedicineIntensity (physics)Vastus lateralis muscleDuration (music)CardiologyAnesthesiaInternal medicinePhysical medicine and rehabilitationSkeletal musclePhysics

Abstract

fetched live from OpenAlex

Few studies have quantified lower limb muscle activity over 24 h using electromyographic signals (EMG). None have described the changes in EMG duration and intensity when data are analyzed with different thresholds. Continuous bilateral EMG recordings were made from vastus lateralis (VL) in 10 subjects (20-48 yr) for 24 h. Before and after this recording, voluntary quadriceps forces and VL EMG at 25%, 50%, 75%, and 100% of the maximal voluntary contraction (MVC), percentage voluntary activation (twitch interpolation), and compound action potentials (M-waves) were recorded. Offline, the 24-h EMG integrals (IEMG, 10-ms time constant) were normalized to the MVC IEMG. Total EMG duration and mean IEMG ranged from 1-3 h and 3.2-12.1% MVC, respectively, when the data were analyzed using the baseline (+3 SD) as threshold. When analysis was done with progressively higher thresholds, from baseline up to 4% MVC, the total EMG duration declined curvilinearly. In some cases the decline in duration was 50-60% for a 1% MVC threshold increment. The mean 24-h IEMG increased by 1.5-2% MVC for each 1% MVC threshold increment. Hence, a small change in the analysis threshold may result in large changes in 24-h EMG duration but moderate changes in mean IEMG. Our findings suggest that VL was active for a short amount of time and at low intensities over 24 h.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.202

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 head0.015
GPT teacher head0.230
Teacher spread0.214 · 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 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

Citations48
Published2009
Admission routes1
Has abstractyes

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