Bibliographic record
Abstract
Interest in arm movements has increased tremendously in recent years. This interest has been motivated by different goals: the desire for a more scientific approach to replacement or support of the joints of the upper limb, the need for input to biomechanical computer models, and the clinical interest in comparing normal movements with pathological movements. The availability of commercial marker-tracking systems has facilitated achieving these goals. However, the complex nature of arm movements and the lack of standardized movements raises many challenges. In comparison with gait analysis, few arm motion analyses have been conducted. The purpose of this review is to aid researchers and clinicians interested in conducting an arm motion study in choosing the appropriate methodology. This is accomplished both by describing the methods used in past investigations and by highlighting important findings. Due to the variety of research goals, there is sometimes more than one appropriate method and the choice is left to the reader. Nevertheless, since it is extremely desirable to record and express the data in a standardized way, standardization proposals are described. This review, which focuses on methodology rather than results, addresses the following topics: motivations and tasks studied, tracking methods, the shoulder complex, joint centres and rotation axes, marker positions, coordinate system definitions, terminology and rotations, accuracy, and presentation methods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".