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Record W2754776992 · doi:10.1109/embc.2017.8037218

Quantifying the effects of on-the-fly changes of seating configuration on the stability of a manual wheelchair

2017· article· en· W2754776992 on OpenAlexaff
Louise Thomas, Jaimie Borisoff, Carolyn J. Sparrey

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsBritish Columbia Institute of TechnologySimon Fraser University
Fundersnot available
KeywordsWheelchairWheelingCenter of gravityAxleManual wheelchairComputer scienceAutomotive engineeringStability (learning theory)SimulationPosition (finance)EngineeringStructural engineering

Abstract

fetched live from OpenAlex

In general, manual wheelchairs are designed with a fixed frame, which is not optimal for every situation. Adjustable on the fly seating allow users to rapidly adapt their wheelchair configuration to suit different tasks. These changes move the center of gravity (CoG) of the system, altering the wheelchair stability and maneuverability. To assess these changes, a computer simulation of a manual wheelchair was created with adjustable seat, backrest, rear axle position and user position, and validated with experimental testing. The stability of the wheelchair was most affected by the position of the rear axle, but adjustments to the backrest and seat angles also result in stability improvements that could be used when wheeling in the community. These findings describe the most influential parameters for wheelchair stability and maneuverability, as well as provide quantitative guidelines for the use of manual wheelchairs with on the fly adjustable seats.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.163
GPT teacher head0.424
Teacher spread0.261 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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