Reliability of Distal Upper Extremity Posture Matching Using Slow-Motion and Frame-by-Frame Video Methods
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the effects of video playback speed on posture matching reliability of the distal upper extremity. BACKGROUND: Video is frequently used in ergonomic assessments, yet there remains a need to determine the effects of viewing speed on posture observations. METHOD: Participants were 7 graduate students experienced with posture-based observational methods. Categorical posture scales were used to evaluate forearm pronation/supination, wrist flexion/extension, wrist radioulnar deviation,and hand activity from workplace video at three playback speeds (quarter, half, and real time). Wrist flexion/extension was also evaluated with a frame-by-frame video method. RESULTS: Posture counts increased with slower viewing speeds for the wrist and hand, but percentage durations in each posture category were similar for all methods. Posture matching interrater reliability scores increased with slow-motion video playback but remained low even for quarter-time video playback. The highest interrater scores were found in the frame-by-frame analysis of wrist flexion/extension for three posture categories (percentage agreement = 84.9% +/- 1.3%; kappa = 0.54 +/- 0.02). CONCLUSION: Although slower video playback speeds increased the number of posture counts for the wrist and hand scales, percentage durations were similar, and reliability scores increased only slightly with slow-motion video playback. APPLICATION: Reviewing video using slow-motion or frame-by-frame methods improves distal upper extremity posture matching reliability. However, ergonomic assessment tools based on percentage duration may not be appreciably enhanced by slowing viewing speed. Thus, the increased viewing time with slower playback should be justified with respect to assessment needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".