A New Localization Algorithm for a Two-Layer Magnetic Sensor Array in Capsule Endoscopy
Bibliographic record
Abstract
Accurate localization of wireless capsule endoscopes (WCE) is essential for gastrointestinal (GI) diagnostics, yet natural peristalsis introduces abrupt capsule motion that challenges conventional optimization-based methods. We propose a magnetic localization framework that integrates a particle filter (PF) with a two-layer sensor array. The PF provides global search capability, reducing sensitivity to poor initial estimates caused by sudden movements, while the two-layer array improves spatial sensitivity and robustness when the capsule shifts away from one layer. Sensor placement and inter-layer separation are further optimized using the Cramér-Rao Lower Bound (CRLB) to minimize uncertainty within the sensing volume. Experimental and simulation results show that the proposed system—optimized for realistic magnet and sensor characteristics-achieves 2 mm position and 2.4° orientation accuracy across a$15 \times 15 \times 25 \text{cm}^{3}$region. Compared with single-layer arrays and local-optimizer-based approaches, the method demonstrates superior accuracy and robustness under dynamic motion, highlighting its potential for practical WCE tracking.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".