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Record W2101889219 · doi:10.1504/ijecb.2009.022862

Precision, repeatability and accuracy of Optotrak<SUP align=right>®</SUP> optical motion tracking systems

2009· article· en· W2101889219 on OpenAlexafffund
J. Schmidt, Devin R. Berg, Heidi‐Lynn Ploeg, Leone Ploeg

Bibliographic record

VenueInternational Journal of Experimental and Computational Biomechanics · 2009
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsKingston General HospitalQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of MinnesotaUniversity of Wisconsin-Madison
KeywordsRepeatabilityTracking (education)Displacement (psychology)Computer visionTilt (camera)Accuracy and precisionMatch movingMotion (physics)Artificial intelligenceComputer scienceBiomedical engineeringMaterials scienceMathematicsEngineeringStatistics

Abstract

fetched live from OpenAlex

In the field of biomechanics, optical motion tracking systems are commonly used to record human motion and assist in surgical navigation. Recently, motion tracking systems have been used to track implant and bone motion on a micron-level. The present study evaluated four different Optotrak® motion tracking systems to determine the precision, repeatability and accuracy under static testing conditions. The distance between the camera systems and the rigid body, as well as the tilt angle of the rigid body, did affect the resulting precision, repeatability and accuracy of the camera systems. The precision and repeatability, calculated as the within-trial and between-trial standard deviations, respectively, were less than 30 µm; with some configurations producing precision and repeatability less than 1 µm. The accuracy was less than 0.53% of the total displacement for the in-plane motion and less than 1.56% of the total displacement for the out-of-plane motion.

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)0.006
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.333
Teacher spread0.318 · 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 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

Citations50
Published2009
Admission routes2
Has abstractyes

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