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Record W1772679128 · doi:10.24908/pceea.v0i0.4047

HUMAN AUDIO/VESTIBULAR SYSTEM: DATA INPUT CHANNELS FOR ROBOTIC FORCE AND MOMENT SENSOR MEASUREMENTS

2011· article· en· W1772679128 on OpenAlexvenueno aff
Sherry Draisey, Mayes Mullins

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceVestibular systemInterface (matter)Artificial intelligenceHuman–computer interactionComputer visionSimulation

Abstract

fetched live from OpenAlex

The design goal was development of an intuitive human machine interface for force and moment data from space robotic operations. This paper defines overall requirements and goals. It describes experimental approaches used to evaluate our ‘nature’ inspired solution. The final portion of the paper discusses the design and prototyping of the segment of the problem which has lead to our first product. One of nature’s ways of presenting multiple degree of freedom (dof), vector data is through our audio and vestibular systems. This directional capability is being applied as a human machine interface (HMI) for robotic force sensing. Human audio direction ability is accurate except for sounds generated above and behind our heads. This inaccuracy has lead us to the development of the vestibulator. The vestibulator is a wireless device which applies low levels of current, to the human subject mastoid bones through surface mounted electrodes. These induce perceptions of tilt. The polarity of the signals provide directional stimulus.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.778
Threshold uncertainty score0.529

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 communication0.0000.001
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 head0.053
GPT teacher head0.249
Teacher spread0.196 · 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
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
Published2011
Admission routes1
Has abstractyes

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