Investigation of permanent magnets in low-cost position tracking
Bibliographic record
Abstract
PURPOSE: Low cost portable ultrasound systems could see improved utility if similarly low cost portable trackers were developed. Permanent magnet based tracking systems potentially offer adequate tracking accuracy in a small workspace suitable for ultrasound image reconstruction. In this study the use of simple permanent magnet tracking techniques is investigated to determine feasibility for use in an ultrasound tracking system. METHODS: Permanent magnet tracking requires finding a position input into a field model which minimizes the error between the measured field, and the field expected from the model. A simulator was developed in MATLAB to determine the effect of sources of error in permanent magnet tracking systems. Insights from the simulations were used to develop a calibration and tracking experiment to determine the accuracy of a simple and low cost permanent magnet tracking system. RESULTS: Simulation and experimental results show permanent magnet based tracking to be highly sensitive to errors in sensor measurements, calibration and experimental setup. The reduction in field strength of permanent magnets lowers with the cube of distance, which leads to very poor signal-to-noise ratios at distances above 20 cm. Small errors in experimental setup also led to high tracking error. CONCLUSION: Permanent magnet tracking was found to be less accurate than is clinically useful, and highly sensitive to errors in sensors and experimental setup. Sensor and calibration limitations make simple permanent magnet tracking systems a poor choice given the present state of sensor technology.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".