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Record W4415438832 · doi:10.1302/1358-992x.2025.10.129

STATIC PRECISION, REPEATABILITY, AND ACCURACY OF AN OPTOTRAK CERTUS™ OPTICAL RIGID BODY TRACKING SYSTEM: IMPLICATIONS FOR IMPLANT MICROMOTION ASSESSMENTS

2025· article· en· W4415438832 on OpenAlexaffabout
Jason Reeves, Gregory W. Spangenberg, Kenneth J. Faber, G. Daniel G. Langohr

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsWestern University
Fundersnot available
KeywordsRepeatabilityStandard deviationPosition (finance)Reliability (semiconductor)Orientation (vector space)Tracking (education)Accuracy and precisionRigid bodyCoordinate-measuring machine

Abstract

fetched live from OpenAlex

Optical tracking is an important biomechanical assessment tool that can quantify the position and orientation of rigid bodies in three-dimensional space that is commonly used to evaluate large- and small-scale displacements. One system commonly used for biomechanical assessments in research labs is the Optotrak Certus™ system (Northern Digital, Waterloo, Canada). This system can monitor marker positions with a reported accuracy of 100μm, however this accuracy is influenced by application-specific factors, and previous investigations have suggested that it may be capable of reliably quantifying much smaller displacements [1]. The purpose of this investigation is to assess the reliability of the Optotrak Certus™ by quantifying its precision, repeatability, and accuracy for use in micromotion investigations. It is hypothesized that the reliability will be better than the 50μm accuracy and 30μm repeatability limits that have previously been proposed for micromotion assessments [1]. Two rigid bodies were mounted to a micrometer test stand used to position the rigid bodies at eight locations in-, then out-of-plane relative to the camera. Both rigid body and camera referencing measurement techniques were assessed. To assess positional precision and repeatability, the test stand was sequentially placed in five pre-defined locations and the static position of the rigid bodies was captured for a duration of 10 seconds, repeated ten times for each location. Positional precision was quantified as the within-trial standard deviation of each of the in-plane and out-of-plane positional coordinates [1][2]. Repeatability was quantified as the between-trial standard deviation of the average position of each of the rigid body's positional coordinates [1][2]. Finally, the test stand was displaced from 0.1–10mm relative to its initial position using the micrometer-controlled test stand, and the static position of the rigid bodies was once again captured. Accuracy was quantified as the difference between the average measured displacement and the expected displacement for each position relative to the initial position. The precision, or within-trial standard deviation of the rigid body's position was poorer when the rigid body referencing method was used, as opposed to direct camera referencing. For these capture techniques, positional precision and repeatability ranged from 0.7μm–14.9μm and 0.8μm–9.2μm, respectively. Repeatability was generally better with the rigid body referencing technique, as opposed to the camera referencing technique ranging between 1.0μm and 3.0μm, as opposed to 0.8μm and 9.2μm, respectively. In- and out-of-plane displacement accuracies ranged from 1.8μm–25.6μm and 2.6μm–29.9μm, respectively, while repeatability ranged from 0.9μm–10.0μm (Figs1,2). The present investigation assessed the Optotrak Certus™ optical tracking system for micro-scale displacements that are relevant to in-vitro assessments of orthopaedic implant subsidence and micromotion. Micro-scale displacements reported through static position captures outperform the 50μm accuracy and 30μm repeatability thresholds that have been suggested for orthopedic micromotion assessments. This was also the first investigation to directly compare how the common practice of referencing the position of one rigid body marker triad relative to another marker impacts positional and displacement reliability, relative to direct camera referencing. For any figures or tables, please contact the authors directly.

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)0.032
metaresearch head score (Gemma)0.104
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.104
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.027
GPT teacher head0.372
Teacher spread0.344 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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