Interpreting the Tilt-Torsion Method to Express Shoulder Joint Kinematics
Bibliographic record
Abstract
Background: Kinematics is studied by practitioners and researchers in different fields of practice. It is therefore critically important to adhere to a taxonomy that explicitly describes positions and movements. However, current representation methods such as cardan and Euler angles fail to report shoulder angles in a way that is easily and correctly interpreted by practitioners, and that is free from numerical instability such as gimbal lock (GL). Methods: In this paper, we comprehensively describe the recent Tilt-Torsion (TT) method and compare it to the Euler YXY method currently recommended by the International Society of Biomechanics. While using the same three rotations as Euler YXY (plane of elevation, elevation, humeral rotation), TT reports humeral rotation independently from the plane of elevation. We assess how TT can be used to describe shoulder angles (1) in a simulated assessment of humeral rotation with the arm at the side, which constitutes a GL position, and (2) during an experimental functional task, with 10 wheelchair basketball athletes who sprint in straight line using a sports wheelchair. Findings: In the simulated GL experiment, TT provided both humeral elevation and rotation measurements, contrary to the Euler YXY method, despite both methods sharing the same GL positions. During the wheelchair sprints, humeral rotation ranged from 14{\deg} (externally) to 13{\deg} (internally), which is consistent with typical maximal ranges of humeral rotation, compared to 65{\deg} to 50{\deg} with the Euler YXY method. Interpretation: Based on our results, we recommend that shoulder angles be expressed using TT instead of Euler YXY.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".