Exploring Vision-Based Technologies for Ergonomic Training in Construction Education
Bibliographic record
Abstract
Musculoskeletal disorders (MSDs) are a critical concern in construction, frequently caused by repetitive motions, awkward postures, and heavy lifting.These risks are prevalent not only in industry but also in educational settings where students engage in handson tasks that mirror real-world conditions.This study investigates the integration of vision-based technologies, specifically Snapshot Ergonomics, into construction education to assess and address ergonomic risks.Snapshot Ergonomics uses video-based motion capture, artificial intelligence (AI), and machine learning (ML) to classify postures as Safe, Cautious, or Hazardous.Three key fabrication activitieswood framing, steel welding, and cladding/finishingwere analyzed with a sample size of nine participants across distinct task variations.Results revealed that neck and elbow postures frequently exhibited hazardous positions, emphasizing the need for ergonomic interventions.This paper discusses the feasibility, scalability, and implications of these tools in improving safety education, ultimately fostering a proactive safety culture in construction.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".