The effect of camera viewing angle on posture assessment repeatability and cumulative spinal loading
Bibliographic record
Abstract
Video-based task analysis in the workplace is often limited by equipment location and production line arrangement, therefore making it difficult to capture the motion in a single plane. The purpose of this study was to investigate the effects of camera placement on an observer's ability to accurately assess working postures in three dimensions and the resultant influence on the reliability and repeatability of calculated cumulative loading variables. Four video cameras were placed at viewing angles of 0 degrees, 45 degrees, 60 degrees and 90 degrees to the frontal plane, enabling the simultaneous collection of views of four lifting tasks (two symmetric and two asymmetric). A total of 11 participants were trained in the use of the 3DMatch 3-D posture matching software package (developed at the University of Waterloo) and were required to analyse 16 lifting trials. Four of the participants were randomly selected to return within 72 h and repeat the analysis protocol to test intra-observer repeatability. Posture matching agreement between camera views was higher when the body segments had a minimal range of motion during the task. There was no significant participant main effect; however, there was a significant (p < 0.05) task main effect. Intraclass correlation coefficients (ICC) were calculated to assess the between day reliability. Compression, reaction anterior shear and extension moment were all found to have excellent reliability (ICC > 0.75). Joint anterior shear and joint posterior shear both provided fair to good reliability (0.4 > ICC < 0.75). Overall, the impact of the camera viewing angle on an observer's ability to match working postural exposure was found to be small.
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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 | 0.009 | 0.058 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".