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Record W4285400512 · doi:10.1109/ur55393.2022.9826259

Touchless Shared-Control for Wheelchair Navigation

2022· article· en· W4285400512 on OpenAlexaff
Graham Ching, Hamed Bozorgi, Trung Dung Ngo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsWheelchairComputer scienceControl (management)Human–computer interactionWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

We present a touchless shared-control approach merging wearable sensor-based control and autonomous safety control for wheelchair navigation in this study. To assist disabled people who have no or limited sensorimotor control with their fingers, a wearable armband sensor is used to control an electric wheelchair. Laser rangefinder based autonomous safety control of obstacle avoidance is also implemented through the learning from demonstration. The shared-control architecture for the wheelchair navigation is built up on the integration of the armband-based human control and autonomous safety control. A customized arbitration function was developed to make smooth transition of two control systems according to user’s preference and satisfaction. Our experiments demonstrate that the touchless shared-control can be applied for wheelchair navigation in the real world.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.240

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 science0.0010.000
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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations2
Published2022
Admission routes1
Has abstractyes

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