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Record W2008652886 · doi:10.1016/s1350-4533(02)00116-9

A wheelchair modified for leg propulsion using voluntary activity or elecrical stimulation

2002· article· en· W2008652886 on OpenAlexafffund
R. B. Stein, D. Roetenberg, Su Ling Chong, Kelvin B. James

Bibliographic record

VenueMedical Engineering & Physics · 2002
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsWheelchairPhysical medicine and rehabilitationHamstringKinematicsTorqueLeverPropulsionBicepsComputer scienceSimulationMedicineEngineeringPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

A commercially available wheelchair has been modified for propulsion by movements of the lower legs. The feet are attached securely to a foot rest that can rotate around the knee joint. Movement is generated either with residual voluntary activation of the quadriceps (knee extensor) and hamstring (knee flexor) muscles, or with electrical stimulation of these muscles, if voluntary control is absent. Either a chain or a lever can couple the movements through a gearbox to the wheel to propel the wheelchair forward. Control of a wheelchair with the legs is more efficient than using the arms and has the potential to increase the mobility and whole-body fitness of many wheelchair users, but there is considerable variability between subjects. To address this variability, we measured for individual subjects the passive properties of the legs and foot at rest (effective stiffness and viscosity), the length-tension (torque-angle) properties of the active muscle groups, as well as their force-velocity curve and their activation and fatigue rates. The measured values were then inserted into a model of the leg-propelled wheelchair. The purpose of this paper is to test whether the model could predict the performance of individual subjects accurately and could be used, for example, to optimize the speed of the wheelchair for a given subject.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.695
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 head0.037
GPT teacher head0.246
Teacher spread0.208 · 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 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

Citations12
Published2002
Admission routes2
Has abstractyes

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