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Record W2141921911 · doi:10.1109/tnsre.2002.802880

Development of an indoor rowing machine with manual FES controller for total body exercise in paraplegia

2002· article· en· W2141921911 on OpenAlexafffund
R. Davoodi, B.J. Andrews, G D Wheeler, Robert Lederer

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2002
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of Alberta
FundersGlenrose Rehabilitation HospitalUniversity of Southern California
KeywordsRowingFunctional electrical stimulationParaplegiaPhysical medicine and rehabilitationTetraplegiaTrunkComputer scienceMedicineSimulationStimulationSpinal cord injury

Abstract

fetched live from OpenAlex

Concept 2 indoor rowing machine (Concept 2 Inc., USA) was modified for functional electrical stimulation (FES) rowing exercise in paraplegia. A new seating system provides trunk stability and constrains the leg motion to the sagittal plane. A 4-channel electrical stimulator activates the quadriceps and hamstrings in Drive and Recovery phases of the rowing cycle, respectively. Two force-sensing resistors (FSR) on the handle measure the thumb press as the command signal to the electrical stimulator. Optical encoders measure the positions of the seat and handle during rowing. To synchronize the voluntarily controlled upper body movement with the FES controlled leg movement, a novel manual control system was developed. It uses the voluntary thumb presses to control the timing of the stimulation to the paralyzed leg muscles. The manual control system was intuitive and easy to learn and resulted in well-coordinated rowing. Evaluation of the modified rower by paraplegic volunteers showed that it is effective, safe, and affordable exercise alternative for paraplegics.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.283
Teacher spread0.265 · 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
GenreMethods

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

Citations74
Published2002
Admission routes2
Has abstractyes

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