Human Temporomandibular Joint Motion: A Synthesis Approach for Designing a Six-Bar Kinematic Simulator
Bibliographic record
Abstract
Abstract The human earcanal can accommodate several types of in-ear devices including hearing aids, earphones, hearing protectors, and earplugs. This canal-type home has a neighbor called the temporomandibular joint (TMJ) whose movements slightly deform the shape of the earcanal. While these cyclic deformations can influence the positioning, comfort, and functioning of ear-fitted devices, they can also provide a significant amount of energy to harvest. Given their importance, the TMJ movements and earcanal deformations have been well studied. However, their mutual actions are still not fully understood. This paper presents the development of a six-bar kinematic TMJ simulator capable of replicating the complicated motion of the jaw. The development relies on a two-phase mechanism design algorithm to numerically optimize and analytically synthesize linkage mechanisms for which the classical optimization approaches cannot return a converged solution. The proposed algorithm enables the design of a kinematic simulator to generate the TMJ path with an average error as low as 1.65% while respecting all the hinge-axis parameters of the jaw. This algorithm can be subsequently used to solve nonlinear complex linkage synthesis problems, and ultimately, the developed kinematic simulator can be used to further investigate TMJ–earcanal interactions.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".