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Record W1971055668 · doi:10.1109/embc.2012.6346325

Gait planning and double support phase model for functional electrical stimulation-based walking

2012· article· en· W1971055668 on OpenAlexaff
Nitin Sharma, R. B. Stein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFunctional electrical stimulationPreferred walking speedBraceGaitComputer sciencePhysical medicine and rehabilitationTrajectorySimulationEffect of gait parameters on energetic costGait analysisStimulationEngineeringPsychologyPhysicsMedicineStructural engineeringNeuroscience

Abstract

fetched live from OpenAlex

Joint or segment angle trajectories of able-bodied persons are often recorded or mimicked as reference trajectories for walking restoration in paraplegia. In this paper, lower limb segment angle trajectories are computed from simple mathematical models developed to represent functional electrical stimulation (FES) and a novel brace based walking. The new models incorporate the double support and single support phases of walking. Dynamic optimization is utilized to design walking trajectories that minimize muscle activations and arm reaction forces generated from the walker. Compared to the voluntary walking trajectories, the new trajectories are more representative of FES-based walking as only a limited number of muscle are stimulated to compute walking trajectories.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.045
GPT teacher head0.286
Teacher spread0.241 · 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 designSimulation or modeling
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

Citations4
Published2012
Admission routes1
Has abstractyes

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