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Record W1980014384 · doi:10.1016/s1350-4533(01)00024-8

The effect of knee-flexion angle on wheelchair turning

2001· article· en· W1980014384 on OpenAlexaff
Angela H. MacPhee, R. Lee Kirby, Adam C. Bell, Donald A. MacLeod

Bibliographic record

VenueMedical Engineering & Physics · 2001
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsWheelchairTraction (geology)Knee flexionManual wheelchairPhysical medicine and rehabilitationInertiaMoment of inertiaBiomechanicsRolling resistanceOrthodonticsMedicineMathematicsPhysical therapyComputer scienceEngineeringStructural engineeringPhysicsAnatomyMechanical engineering

Abstract

fetched live from OpenAlex

The increasingly popular hyperflexed knee-flexion angle was evaluated to determine its effects on wheelchair turning. Twenty able-bodied subjects were tested comparing the effect of full knee extension and full knee flexion on a number of parameters. We empirically measured the angular velocity of subjects spinning 720 degrees in place, subjects' perceived ease of wheelchair turning, the overall length of the wheelchair, the anteroposterior position of the center of mass (COM), rolling resistance, turning resistance and rear-wheel traction. The combined moment of inertia of the wheelchair and system was modeled. We found that, in comparison with full extension, fully flexing the knees increased angular velocity by 40% and was perceived to be 66% easier by subjects. Overall length decreased by 39%, COM moved rearward 38%, rolling and turning resistance decreased by 21% and 17% respectively, rear-wheel traction increased by 12% and moment of inertia decreased by 42%. All empirically tested parameters were statistically significant (p<0.007). We conclude that the knee-flexion angle has a significant effect on wheelchair turning. The implications of these findings for wheelchair design and prescription will need to be validated on actual wheelchair users and for smaller increments in knee-flexion range.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.012
GPT teacher head0.312
Teacher spread0.300 · 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 designOther design
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

Citations13
Published2001
Admission routes1
Has abstractyes

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