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Record W2078058557 · doi:10.1115/sbc2009-206505

Subject-Specific EMG Pattern Classification for Effective Rehabilitation of Stroke Survivors

2009· article· en· W2078058557 on OpenAlexfundno aff
Sang Wook Lee, Kristin Wilson, Blair A. Lock, Todd Kuiken, Derek G. Kamper

Bibliographic record

VenueASME 2009 Summer Bioengineering Conference, Parts A and B · 2009
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsnot available
FundersMcMaster University
KeywordsPhysical medicine and rehabilitationRehabilitationElectromyographyStroke (engine)Motor impairmentChronic strokeMedicinePhysical therapyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Many stroke survivors experience chronic upper extremity impairment that leads to significant functional limitations. Especially for those with more severe impairment, therapeutic treatment may have limited effect. Thus, the introduction of assistive techniques, such as powered orthoses, may prove more beneficial in improving function. A challenge, however, lies in providing volitional control of the device to the user. Electromyography (EMG) of muscle has been used with stroke survivors to trigger myoelectric prostheses [1], but the number of the targeted motions and their complexity was limited.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.235
Teacher spread0.217 · 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 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

Citations0
Published2009
Admission routes1
Has abstractyes

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Same venueASME 2009 Summer Bioengineering Conference, Parts A and BSame topicMuscle activation and electromyography studiesFrench-language works237,207