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Record W4411407833 · doi:10.57041/cf3geg55

Integration of Hand Gesture and Voice Command Control for Smart Wheelchairs

2024· article· en· W4411407833 on OpenAlexaff
Muhammad Farhan Shahid, Asad Ali, Faheem Ashraf

Bibliographic record

VenueInternational Journal of Emerging Engineering and Technology · 2024
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsMemorial University of Newfoundland
FundersGovernment College University, Lahore
KeywordsWheelchairGestureGesture recognitionComputer scienceHuman–computer interactionControl (management)Assistive technologyIndependence (probability theory)Limit (mathematics)ArchitectureArtificial intelligence

Abstract

fetched live from OpenAlex

The development of smart wheelchair systems has gained significant attention due to the increasing demand for assistive technologies that enhance the independence and mobility of individuals with disabilities. Traditional wheelchair control methods often limit the user’s ability to perform tasks with ease. This paper proposes an innovative dual-mode control system that integrates both hand gesture recognition and voice command technology to enable more intuitive, responsive, and adaptive control of wheelchairs. The proposed system combines the strengths of both gesture and voice recognition to provide a robust solution that adapts to the needs of users with various disabilities. A detailed description of the system architecture, implementation, and evaluation is provided.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.799
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.243
Teacher spread0.237 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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