Development of a Complete Upper Extremity Model for Assessment of Shoulder, Elbow, Wrist, and Finger Motion
Bibliographic record
Abstract
Upper extremity function is crucial to many activities of daily living, as well as to an individual's level of independence and quality of life. Several neurological disorders and diseases such as muscular dystrophy, spinal cord injury and stroke can negatively affect upper extremity strength and motion. According to the Public Health Agency of Canada, in 2013, approximately 741,800 Canadians were living with the effects of a stroke. It is estimated that between 50-80% of this population had to undergo some form of rehabilitation in order to regain movement and strength in upper limbs. A better understanding of human movement may improve treatment methods and evaluation of patient progress. The use of stereo-photogrammetry motion capture, for instance, can provide accurate quantitative information on upper extremity kinematics using comprehensive mechanical models. This information can help clinicians provide more effective treatment strategies. Most upper extremity kinematic models used in biomechanical and clinical research today do not include finger and thumb segments due to their complexity. In order to evaluate hand functionality, a hand kinematic model must be used separately. However, in the rehabilitation field, it has been shown that improving hand and wrist function improves how a patient moves their, shoulders, and elbow. For this reason, when using motion capture to evaluate the progress of a patient with loss of hand motor ability, having a kinematic model that assesses shoulder, elbow, wrist, and finger joint motion is of paramount importance. The aim of this research was to develop and test the reliability of a complete upper extremity kinematic model, including the finger and thumb segments, that is feasible and clinically meaningful for the evaluation of upper extremity and finger motion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".