Monocular Vision–Based Framework for Biomechanical Analysis or Ergonomic Posture Assessment in Modular Construction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Awkward and improper postures and motions reduce productivity and increase project costs in the modular construction industry. Ergonomic assessment is essential to identify, mitigate, and prevent these postures for safety and productivity improvement. Advanced computer vision technologies have made vision–based ergonomic assessment cost-effective in real workplaces. However, their accuracy and robustness still need to be improved. This paper proposes a monocular vision–based framework for conducting a biomechanical analysis or ergonomic posture assessment. The framework consists of four components: worker visual tracking, two-dimensional (2D) joint and body part detection, 2D joints refinement, and three-dimensional (3D) body model generation and joint angle calculation. The framework has been tested with videos recorded in real construction workshops. The results show that the framework could use the videos from a single camera to estimate a total of 14 joint angles with the average error of 11° and identify workers’ awkward postures and motions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it