Electromagnetic tracking performance analysis and optimization
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to evaluate the uncertainties of an electromagnetic (EM) tracking system and to improve both the trueness and the precision of the EM tracker. METHODS: For evaluating errors, we introduce an optical (OP) tracking system and consider its measurement as "ground truth". In the experiment, static data sets and dynamic profiles are collected in both relatively less-metallic environments. Static data sets are for error modeling, and dynamic ones are for testing. To improve the trueness and precision of the EM tracker, tracker calibration based on polynomial fitting and smooth filters, such as the Kalman filter, the moving average filter and the local regression filter, are deployed. RESULTS: From the experimental data analysis, as the distance between the transmitter and the sensor of the EM tracking system increases, the trueness and precision tend to decrease. The system's trueness and jitter errors can be modeled as the 3(rd) order polynomial error equations. After minimizing the positional error and applying smoothing filters, the mean value of error reduction is 36.9%. CONCLUSION: Our method can effectively reduce both positional systematic error and jitter error caused by EM field distortion. The method is successfully applied to calibrate an EM tracked surgical cautery tool.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".