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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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.786

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 head0.018
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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