Six degrees of freedom intrafraction cranial motion detection using a novel capacitive monitoring technique: evaluation with human subjects
Bibliographic record
Abstract
The purpose of this work is to introduce and evaluate a capacitive monitoring array capable of continuous 6DOF cranial motion detection during high precision radiotherapy. The ring-shaped capacitive array consists of four equally sized conductive sensors positioned at the cranial vertex. The system is modular, non-contact, and provides continuous motion information through the thermoplastic immobilization mask without relying on skin monitoring or use of ionizing radiation. The array performance was evaluated through a volunteer study with a cohort of twenty-five individuals. The study was conducted in a linac suite and the volunteers were fitted with an S-frame thermoplastic mask. Each volunteer took part in one data acquisition session per day for three consecutive days. During the data acquisition, the conductive array was translated and rotated relative to their immobilized cranium in 1-millimetre and 1-degree steps to simulate cranial motion. Capacitive signals were collected at each position at a frequency of 20 Hz. The data from the first acquisition session was then used to train a classifier model and establish calibration equations. The classifier and calibration equations were then applied to data from the subsequent acquisition sessions to evaluate the system performance. The trained classifiers had an average success rate of 92.6% over the volunteer cohort. The average error associated with calibration had a mean value below 0.1 mm or 0.1 deg for all six motions. The capacitive array system provides a novel method to detect translational and rotational cranial motion through a thermoplastic mask.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".