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Record W1974279208 · doi:10.1145/1579114.1579129

Communicating through gestures without visual feedback

2009· article· en· W1974279208 on OpenAlexafffund
Irene Sturm, Maria Schiewe, Wiebke Köhlmann, Helmut Jürgensen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaBundesministerium für Wirtschaft und Technologie
KeywordsGestureHuman–computer interactionComputer scienceHaptic technologyWearable computerVisual feedbackInterface (matter)Wearable technologyGesture recognitionMultimediaArtificial intelligenceEmbedded system

Abstract

fetched live from OpenAlex

Human-machine interaction by gestures helps to improve communication: it utilizes means of communication which are common to humans but alien to machines. We consider ergonomic, functional and semantic issues in gesture-based interfaces without visual feedback. Such situations arise with the unobtrusive usage of wearable devices but, more importantly, with interfaces for visually impaired persons. We consider the latter scenario -- that of blind users -- in view of forthcoming haptic interface technology and investigate properties of gestural interaction models.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.480

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 head0.068
GPT teacher head0.368
Teacher spread0.299 · 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 designBench or experimental
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

Citations9
Published2009
Admission routes2
Has abstractyes

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