A scoping review on emerging technologies and automation of musculoskeletal ergonomic assessments
Bibliographic record
Abstract
Ergonomic suitability is critical in tasks involving musculoskeletal movement. Many industries have examined best practices and assessed workers' ergonomic conditions during physical tasks. Prolonged awkward postures are a known cause of discomfort and restricted mobility in areas such as the arms, spine, and neck. Technologies like computer vision and human activity recognition can help identify and prioritize ergonomic improvements. This paper presents findings from a two-decade scoping review on the role of automation and study design in ergonomic assessments of physical workplace tasks. Articles were sourced from Scopus, PubMed, IEEE Xplore, Engineering Village, and Google Scholar. Eighty-four studies were analyzed to evaluate the use of technologies in data collection, experimentation, analysis, and validation. We examined how participant variables (e.g. sample size, body part of interest) and validation accuracy impact study outcomes across domains. Integrating advanced technologies into ergonomic evaluations can enhance worker safety and productivity by supporting real-time, evidence-based decision-making.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".