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Record W2167732087 · doi:10.1109/icorr.2005.1501078

The Laser Line Object Detection Method in an Anti-Collision System for Powered Wheelchair

2005· article· en· W2167732087 on OpenAlexaff
Hai Huang, Geoff Fernie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsToronto Rehabilitation Institute
Fundersnot available
KeywordsWheelchairCollisionLine (geometry)Computer scienceObject (grammar)Object detectionComputer visionSimulationArtificial intelligencePattern recognition (psychology)Computer securityMathematics

Abstract

fetched live from OpenAlex

The residents in long term care facilities with cognitive impairment and mobility disability need an anti-collision system on their powered wheelchairs to prevent them from causing other seniors to fall. Because of the severe consequence of falling, the detection method of the anti-collision system must be very reliable. However, many object detection techniques tend to miss targets that are unfavourably oriented or have certain surface properties. This research evaluated an uncommon method: laser line object detection (LLOD). The LLOD system projects an invisible infrared laser line onto the ground, and reads the resulting image via a camera. By analyzing the laser line in the image, the system can identify whether objects are in the target area. A pilot LLOD system was designed and installed on a powered wheelchair. The results of the evaluation experiments showed that the LLOD system can detect almost all obstacles with different orientations and materials, and produced a high detection rate on favourable flooring surfaces.

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)0.000
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: none
Teacher disagreement score0.939
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.017
GPT teacher head0.298
Teacher spread0.280 · 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

Citations6
Published2005
Admission routes1
Has abstractyes

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