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Record W2167584018 · doi:10.1109/tnsre.2007.891385

An Intelligent Powered Wheelchair to Enable Mobility of Cognitively Impaired Older Adults: An Anticollision System

2007· article· en· W2167584018 on OpenAlexaff
Alex Mihailidis, Pantelis Elinas, Jennifer Boger, Jesse Hoey

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2007
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsWheelchairPhysical medicine and rehabilitationComputer scienceCognitionObject (grammar)SimulationHuman–computer interactionPsychologyMedicineArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Older adults with cognitive impairments are generally prohibited from using powered wheelchairs, because of the high risk of collisions with people and objects. This paper describes and presents the preliminary results of a system that uses an infrared sensor to provide anticollision and a prompting system for a powered wheelchair that helps guide the user safely past obstacles. Trials with the prototyped system detected collisions and stopped the chair in 95% of trials with an object and generated no false alarms.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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

Citations59
Published2007
Admission routes1
Has abstractyes

Explore more

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