A Comparison of Methods for Decoupling Tongue and Lower Lip From Jaw Movements in 3D Articulography
Bibliographic record
Abstract
PURPOSE: One popular method to study the motion of oral articulators is 3D electromagnetic articulography. For many studies, it is important to use an algorithm to decouple the motion of the tongue and the lower lip from the motion of the mandible. In this article, the authors describe and compare 4 methods for decoupling jaw motion by using 3D tongue and lower lip data. METHOD: A 3D position estimation method (3DPE), an adapted version of the estimated rotation method (ERM) proposed by Westbury, Lindstrom, and McClean (2002) for 3D recordings, a linear subtraction method, and a new method called Jaw and Oral Analysis (JOANA) were evaluated with data recorded from sensors attached to the lower molars, lower lip, and tongue. RESULTS: The 3DPE method showed the fewest errors. However, unlike the other methods, it requires more than one sensor attached to the lower jaw. Among the single-sensor methods, JOANA was found to be the most comparable to 3DPE. CONCLUSION: The findings suggest that JOANA is efficient in decoupling tongue and lower lip motion from jaw motion, whereas ERM, with its less complicated procedure for attaching the lower jaw incisor sensor, can be considered a viable alternative.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".