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Computer Interface by Gesture and Voice for Users with Special Needs

2006· article· en· W2154923718 on OpenAlexaff
Denyse I. LeBlanc, YasserA Ahmed, Sid‐Ahmed Selouani, Yassine Bouslimani, Habib Hamam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsGestureComputer scienceGesture recognitionHuman–computer interactionCursor (databases)Interface (matter)User interfaceInput deviceHead (geology)Voice command deviceSpeech recognitionArtificial intelligenceComputer hardware

Abstract

fetched live from OpenAlex

We propose an infrared sensor based cost effective human-machine interface interpreting the user's hand or head gestures. This system targets people suffering from reduced mobility as well as specific professionals operating in constraining situations. This design allows using simple head movements to perform basic computer mouse operations, such as moving the mouse cursor on a computer screen. To improve functionality, the system uses voice recognition to execute repetitive movements, such as clicks and double-clicks. We explain two different infrared sensor layouts and discuss the way our design detects and processes a user's head or hand movements. Furthermore, we explain the benefits and drawbacks of using voice recognition in our design. The conclusion presents our results as well as future perspectives for our design

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.750
Threshold uncertainty score0.303

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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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 designNot applicable
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

Citations1
Published2006
Admission routes1
Has abstractyes

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