An analysis of functional shoulder movements during task performance using Dartfish movement analysis software
Bibliographic record
Abstract
PURPOSE: Video-based movement analysis software (Dartfish) has potential for clinical applications for understanding shoulder motion if functional measures can be reliably obtained. The primary purpose of this study was to describe the functional range of motion (ROM) of the shoulder used to perform a subset of functional tasks. A second purpose was to assess the reliability of functional ROM measurements obtained by different raters using Dartfish software. MATERIALS AND METHODS: Ten healthy participants, mean age 29 ± 5 years, were videotaped while performing five tasks selected from the Disabilities of the Arm, Shoulder and Hand (DASH). Video cameras and markers were used to obtain video images suitable for analysis in Dartfish software. Three repetitions of each task were performed. Shoulder movements from all three repetitions were analyzed using Dartfish software. The tracking tool of the Dartfish software was used to obtain shoulder joint angles and arcs of motion. Test-retest and inter-rater reliability of the measurements were evaluated using intraclass correlation coefficients (ICC). RESULTS: Maximum (coronal plane) abduction (118° ± 16°) and (sagittal plane) flexion (111° ± 15°) was observed during 'washing one's hair;' maximum extension (-68° ± 9°) was identified during 'washing one's own back.' Minimum shoulder ROM was observed during 'opening a tight jar' (33° ± 13° abduction and 13° ± 19° flexion). Test-retest reliability (ICC = 0.45 to 0.94) suggests high inter-individual task variability, and inter-rater reliability (ICC = 0.68 to 1.00) showed moderate to excellent agreement. CONCLUSION: KEY FINDINGS INCLUDE: 1) functional shoulder ROM identified in this study compared to similar studies; 2) healthy individuals require less than full ROM when performing five common ADL tasks 3) high participant variability was observed during performance of the five ADL tasks; and 4) Dartfish software provides a clinically relevant tool to analyze shoulder function.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".