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Record W2748190462 · doi:10.1080/10400435.2017.1366373

Starting and stopping kinetics of a rear mounted power assist for manual wheelchairs

2017· article· en· W2748190462 on OpenAlexafffund
Stephanie Wong, W. Ben Mortenson, Bonita Sawatzky

Bibliographic record

VenueAssistive Technology · 2017
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsInternational Collaboration On Repair DiscoveriesVancouver Coastal HealthVancouver Coastal Health Research InstituteUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsWheelchairTreadmillManual wheelchairSimulationComputer sciencePropulsionPhysical medicine and rehabilitationEngineeringPhysical therapyMedicine

Abstract

fetched live from OpenAlex

A rear mounted, powered, drive wheel has been developed to assist with mobility for manual wheelchairs. The version tested operates in two modes (indoor and outdoor). To start in the indoor mode users must initiate propulsion with sufficient force to trigger the motor. To stop users must apply a braking force through the handrims. . The objectives of this study were to compare (1) the minimum force required to start a wheelchair with and without the drive, and (2) the distances and forces needed to stop a wheelchair at different treadmill speeds with and without the device. We used a crossover study design with 24 able-bodied persons. The main outcome measures were starting force single push speed, stopping distance, and stopping force. Participants did not have significantly increased starting force or single push speed using the drive. Participants had significantly shorter absolute stopping distance (p = 0.045 and reduced stopping force (p = 0.02) using the add-on at both treadmill speeds. Given the decreased stopping distances, the add-on may be a viable option for wheelchair users with limited upper limb strength.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

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)0.0030.000

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 head0.040
GPT teacher head0.414
Teacher spread0.374 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

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