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Record W2136558971 · doi:10.1109/iembs.2002.1053331

Manual and automatic control of FES-assisted indoor rowing exercise

2002· article· en· W2136558971 on OpenAlexaff
R. Davoodi, B.J. Andrews, G D Wheeler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRowingFunctional electrical stimulationPhysical medicine and rehabilitationAutomatic controlController (irrigation)Movement (music)StimulationComputer scienceSimulationEngineeringMedicineControl engineering

Abstract

fetched live from OpenAlex

An indoor rowing exercise system has been developed for paraplegics in which voluntary upper body movements are combined with electrically stimulated movements of the paralyzed leg muscles for total body exercise. Manual and automatic functional electrical stimulation (FES) control strategies have been used to control the stimulation intensities and coordinate the voluntary and FES generated movements. In manual control, voluntary button presses control the timing of stimulation while in automatic control a two-level hierarchical controller manages the movement coordination and stimulation delivery to the paralyzed muscles. Paraplegic volunteers found both controllers intuitive and easy to use. Automatic control spends more electrical stimulation per rowing cycle but it is more convenient and natural to use, continuous concentration on the movement and repeated thumb presses are not necessary, and it extends the user base of the FES rowing to paraplegics with higher level injury who can not press on the buttons but can pull/push the handlebar.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0040.000

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 head0.010
GPT teacher head0.200
Teacher spread0.190 · 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

Citations11
Published2002
Admission routes1
Has abstractyes

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