Accuracy assessment and interpretation for optical tracking systems
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.
Bibliographic record
Abstract
Highly accurate spatial measurement systems are among the enabling technologies that have made image-guided surgery possible in modern operating theaters. Assessing the accuracies of such systems is subject to much ambiguity, though. The underlying mathematical models that convert raw sensor data into position and orientation measurements of sufficient accuracy complicate matters by providing measurements having non-uniform error distributions throughout their measurement volumes. Users are typically unaware of these issues, as they are usually presented with only a few specifications based on some "representative" statistics that were themselves derived using various data reduction methods. As a result, much of the important underlying information is lost. Further, manufacturers of spatial measurement systems often choose protocols and statistical measures that emphasize the strengths of their systems and diminish their limitations. Such protocols often do not reflect the end users' intended applications very well. Users and integrators thus need to understand many aspects of spatial metrology in choosing spatial measurement systems that are appropriate for their intended applications. We examine the issues by discussing some of the protocols and their statistical measures typically used by manufacturers. The statistical measures for a given protocol can be affected by many factors, including the volume size, region of interest, and the amount and type of data collected. We also discuss how different system configurations can affect the accuracy. Single-marker and rigid body calibration results are presented, along with a discussion of some of the various factors that affect their accuracy. Although the findings presented here were obtained using the NDI Polaris optical tracking systems, many are applicable to spatial measurement systems in general.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it