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Record W1974694086 · doi:10.1109/iembs.2010.5627632

Three-dimensional gesture comparison using curvature analysis of position and orientation

2010· article· en· W1974694086 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCurvatureOrientation (vector space)Computer visionComputer scienceQuaternionArtificial intelligenceGestureInvariant (physics)Position (finance)MathematicsGeometry

Abstract

fetched live from OpenAlex

This paper describes a new analysis method dedicated to the comparison of human gestures. The orientations and the positions of the gestures are first digitized using active 3D sensors and then compared to a 6-D template using curvature analysis. The proposed algorithm first starts by computing the invariant curvature of 3D position and orientation of a surgical tool using Frenet-Serret frames in 3D and quaternion space. The resulting curvature calculation is matched and compared to the template using a Dynamic Time Warping method. The proposed method is invariant to sensor position and orientation. An experimental study shows the efficiency of the new algorithm for an application in obstetrics, where the aim is to compare forceps blade placements between a senior medical doctor and a novice.

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.

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: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.212

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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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

Quick stats

Citations9
Published2010
Admission routes1
Has abstractyes

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