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Record W2120875587 · doi:10.1109/tnsre.2004.842366

Utilization of ultrasound sensors for anti-collision systems of powered wheelchairs

2005· article· en· W2120875587 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2005
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsToronto Rehabilitation InstituteSunnybrook Health Science Centre
Fundersnot available
KeywordsWheelchairCollisionComputer scienceUltrasonic sensorObject (grammar)UltrasoundSimulationCollision avoidanceHuman–computer interactionReal-time computingArtificial intelligenceAcousticsComputer securityPhysics

Abstract

fetched live from OpenAlex

Anti-collision systems have been developed for use with powered wheelchairs in order to enable people with cognitive or physical impairments to safely operate a powered wheelchair. Anti-collision systems consist of sensors that have the ability to detect objects near the wheelchair and a computer that can stop the chair if a collision is determined to be likely. This investigation considered the suitability of using ultrasound sensors in such a system when encountering objects typically found within a home or a long-term care facility. An ultrasound sensor's ability to detect an object was dependent on the object's size, shape, specularity, reflectivity, and sound absorption characteristics. Ultrasound sensors, by themselves, were found to be unsuitable for anti-collision systems due to an inability to detect objects commonly encountered in the target environment (the home or long-term care facility) without increasing the complexity of the system to such a degree that it would be prohibitive to deploy this technology to the public.

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.

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 score0.596
Threshold uncertainty score0.456

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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