Wrist Posture Estimation Differences and Reliability Between Video Analysis and Electrogoniometer Methods
Bibliographic record
Abstract
Objective The aim of this study was to determine the inter- and intrarater agreement of estimated wrist angles using video and to compare wrist angles from video analysis to electrogoniometers. Background Video analysis is used frequently in ergonomic assessments, but factors including parallax and complex angles may influence wrist angle estimates. Electrogoniometers are an alternative to video, but may not be reliable in complex postures. Given the limitations of each method, there is a need to determine the suitability of the measurement methods for field use. Method Ten participants performed frame-by-frame wrist (flexion–extension, radioulnar deviation) and forearm (pronation–supination) posture estimation for worker tasks from three camera views (top, side, and oblique). Workers were equipped with electrogoniometers to record wrist posture during the tasks. The video estimate data was compared between 2 days and to sensor data. Results Percent agreement between participants ranged from 53% to 81% across all ratings. Agreement was highest from the side view (66%, κ = 0.56) for flexion–extension and top view for radioulnar deviation (77%, κ = 0.52) and pronation–supination (69%, κ = 0.58). Video–electrogoniometer agreement was lower, with peak agreement from the top view for flexion–extension (57%, κ = 0.49) and radioulnar deviation (68%, κ = 0.30) and the oblique view for pronation–supination (53%, κ = –0.1). Conclusion Participant estimate agreement was moderate-substantial overall and aligns with previous reports. Disagreement between video and electrogoniometers may be attributed to camera angle and parallax effects and the small magnitude of wrist motions compared to other joints.
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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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".