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Record W2021333115 · doi:10.2298/jac1301027b

Walking with functional electrical stimulation and unlocking braces in thoracic-level paraplegia

2013· article· en· W2021333115 on OpenAlexafffund
Jacques Bobet, SuLing Chong, Robert Rolf, Richard B. Stein

Bibliographic record

VenueJournal of Automatic Control · 2013
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of Alberta
FundersAlberta Innovates
KeywordsPhysical medicine and rehabilitationTrunkKinematicsFunctional electrical stimulationParaplegiaAnklePreferred walking speedMedicineHeart rateSwingGround reaction forceBalance (ability)StimulationPhysical therapyPhysicsAnatomySpinal cord

Abstract

fetched live from OpenAlex

Walking was tested in 4 people with thoracic-level paraplegia using stimulation of quadriceps muscles, the flexor reflex and unlocking knee-ankle-foot orthoses (KAFO). Heart rate, speed, distance, kinematics and ground reaction forces were measured while subjects walked using a walker. None of the subjects could walk without the system; all could walk continuously for at least 4 minutes with it. Joint angles and some other kinematic features resembled normal walking, but the walking was too slow (average speed: 3.8 m/min.) and too demanding (heart rate: 128 b/min; physiological cost index: 15 b/m) to be practical. Subjects supported about 1/3 of their weight with their arms during stance and about 2/3 during swing. Our results suggest that the braces reduced the effort needed and that the low speeds were due to both a lack of power at push-off and the time needed to stabilize the hip and trunk. The high heart rates arose from excessive contraction of the arm and trunk muscles for balance and propulsion.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.017
GPT teacher head0.265
Teacher spread0.249 · 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
Published2013
Admission routes2
Has abstractyes

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