Comparative tracking error analysis of five different optical tracking systems
Bibliographic record
Abstract
OBJECTIVE: Effective utilization of an optical tracking system for image-based surgical guidance requires optimal placement of the dynamic reference frame (DRF) with respect to the tracking camera. Unlike other studies that measure the overall accuracy of a particular navigation system, this study investigates the precision of one component of the navigation system: the optical tracking system (OTS). The precision of OTS measurements is quantified as jitter. By measuring jitter, one can better understand how system inaccuracies depend on the position of the DRF with respect to the camera. MATERIALS AND METHODS: Both FlashPointtrade mark (Image Guided Technologies, Inc., Boulder, Colorado) and Polaristrade mark (Northern Digital Inc., Ontario, Canada) optical tracking systems were tested in five different camera and DRF configurations. A linear testing apparatus with a software interface was designed to facilitate data collection. Jitter measurements were collected over a single quadrant within the camera viewing volume, as symmetry was assumed about the horizontal and vertical axes. RESULTS: Excluding the highest 5% of jitter, the FlashPoint cameras had an RMS jitter range of 0.028 +/- 0.012 mm for the 300 mm model, 0.051 +/- 0.038 mm for the 580 mm model, and 0.059 +/- 0.047 mm for the 1 m model. The Polaris camera had an RMS jitter range of 0.058 +/- 0.037 mm with an active DRF and 0.115 +/- 0.075 mm with a passive DRF. CONCLUSION: Both FlashPoint and Polaris have jitter less than 0.11 mm, although the error distributions differ significantly. Total jitter for all systems is dominated by the component measured in the axis directed away from the camera.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".