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Record W2145009427 · doi:10.1088/0266-5611/24/6/065010

Computed myography: three-dimensional reconstruction of motor functions from surface EMG data

2008· article· en· W2145009427 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInverse Problems · 2008
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTikhonov regularizationElectrical impedance myographyRegularization (linguistics)SmoothnessBicepsMathematicsFinite element methodSpinal cord injuryRehabilitationInverse problemBiomedical engineeringComputer sciencePhysical medicine and rehabilitationMathematical analysisArtificial intelligenceSpinal cordMedicinePhysical therapyStructural engineeringEngineering

Abstract

fetched live from OpenAlex

We describe a methodology called computed myography (CMG) to qualitatively and quantitatively determine the activation level of individual muscles by voltage measurements from an array of voltage sensors on the skin surface. A finite element model for electrostatics simulation is constructed from morphometric data. For the inverse problem we utilize a generalized Tikhonov regularization. This imposes smoothness on the reconstructed sources inside the muscles and suppresses sources outside the muscles using a penalty term. Results from experiments with simulated and human data are presented for activation reconstructions of three muscles in the upper arm (biceps brachii, bracialis, and triceps). This approach potentially offers a new clinical tool to sensitively assess muscle function in patients suffering from neurological disorders (e.g., spinal cord injury), and could more accurately guide advances in the evaluation of specific rehabilitation training regimens. 1

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.

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 score0.404
Threshold uncertainty score0.588

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 head0.041
GPT teacher head0.209
Teacher spread0.168 · 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