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Record W2809725077

Improving Wrist Force Estimation With Surface EMG During Isometric Contractions

2018· article· en· W2809725077 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

VenueCMBES Proceedings · 2018
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsBrachioradialisIsometric exerciseBicepsSIGNAL (programming language)ElectromyographyComputer scienceSpectral densityMathematicsStatisticsAnatomyPhysical medicine and rehabilitationMedicine
DOInot available

Abstract

fetched live from OpenAlex

In this paper, methods for selecting channels to improve estimated force using fast orthogonal search (FOS) have been investigated and a new method is proposed. The surface electromyogram (sEMG) signal acquired from linear surface electrode arrays, placed on the long head and short head of biceps brachii and brachioradialis during isometric contractions are used to estimate force induced at wrist using the FOS algorithm. In this paper, the effects of the sEMG signal characteristics obtained from the arrays and channels’ locations on the estimated force are investigated to find channels resulting in force estimation improvement compared to using all available channels. Several methods for channel selection have been studied, showing that the sensitivity of the estimated force to the location of the channels is subject-dependent. The proposed method uses only the channels with highest mean of power spectrum density (PSD) and low cross correlations. The channels selected by this method have improved FOS force estimate compared to using all the available channels.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.576

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 communication0.0000.001
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 head0.006
GPT teacher head0.198
Teacher spread0.193 · 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