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Record W2011350558 · doi:10.1145/2702123.2702133

Myopoint

2015· article· en· W2011350558 on OpenAlexaff
Faizan Haque, Mathieu Nancel, Daniel Vogel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicHand Gesture Recognition Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInertial measurement unitComputer scienceComputer visionPointer (user interface)AccelerometerArtificial intelligenceInertial frame of referenceAccelerationPhysics

Abstract

fetched live from OpenAlex

We describe a mid-air, barehand pointing and clicking interaction technique using electromyographic (EMG) and inertial measurement unit (IMU) input from a consumer armband device. The technique uses enhanced pointer feedback to convey state, a custom pointer acceleration function tuned for angular inertial motion, and correction and filtering techniques to minimize side-effects when combining EMG and IMU input. By replicating a previous large display study using a motion capture pointing technique, we show the EMG and IMU technique is only 430 to 790 ms slower and has acceptable error rates for targets greater than 48 mm. Our work demonstrates that consumer-level EMG and IMU sensing is practical for distant pointing and clicking on large displays.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.288
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2880.132

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 head0.062
GPT teacher head0.258
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations101
Published2015
Admission routes1
Has abstractyes

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