Accuracy assessment protocols for electromagnetic tracking systems
Bibliographic record
Abstract
Electromagnetic tracking systems have found increasing use in medical applications during the last few years. As with most non-trivial spatial measurement systems, the complex determination of positions and orientations from their underlying raw sensor measurements results in complicated, non-uniform error distributions over the specified measurement volume. This makes it difficult to unambiguously determine accuracy and performance assessments that allow users to judge the suitability of these systems for their particular needs. Various assessment protocols generally emphasize different measurement aspects that typically arise in clinical use. This can easily lead to inconclusive or even contradictory conclusions. We examine some of the major issues involved and discuss three useful calibration protocols. The measurement accuracy of a system can be described in terms of its 'trueness' and its 'precision'. Often, the two are strongly coupled and cannot be easily determined independently. We present a method that allows the two to be disentangled, so that the resultant trueness properly represents the systematic, non-reducible part of the measurement error, and the resultant precision (or repeatability) represents only the statistical, reducible part. Although the discussion is given largely within the context of electromagnetic tracking systems, many of the results are applicable to measurement systems in general.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.082 | 0.299 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".