Observation-based posture assessment: review of current practice and recommendations for improvement.
Bibliographic record
Abstract
This report describes an observational approach for assessing postural stress of the trunk and upper limbs that is intended to improve risk analysis for prevention of musculoskeletal disorders.The approach is supported by several recent research studies.These studies have evaluated how much time it takes observers to classify specific trunk and upper limb postures, how frequently observers are likely to make posture classification errors, and the magnitude of these errors.The frequency and magnitude of posture classification errors depend on how many categories (levels) are available from which to classify the specific posture.Recent studies suggest that optimal posture analysis performance is obtained by partitioning trunk flexion range of motion into 4 categories of 30° increments; trunk lateral bend into 3 categories of 15° increments; shoulder flexion into 5 categories of 30°; shoulder abduction into 5 categories of 30°; and elbow flexion into 4 categories of 30°.These categories are suggested because they optimize how rapidly and effectively analysts can visually judge posture.This report also presents more general guidelines for the video recording of posture and for the posture analysis process.Guidelines for video recording address such factors as camera position, field of view, lighting, and duration of recording.Guidelines for posture analysis address enhancements such as the benefits of digital video, computer software, training, and use of visual reference and perspective cues.Information in this report can assist health/safety, ergonomics, and risk management/loss control practitioners who conduct job/ worksite assessments of lifting, pushing, pulling, carrying, and/or manual handling risk factors.
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".