Evaluating the prospective benefit of considering movement variability in ergonomic risk assessment
Bibliographic record
Abstract
Digital human models used for ergonomics analysis tend to be deterministic, predicting a single movement strategy and corresponding biomechanical exposures using either regression or optimization methods. The deterministic nature of these existing tools may limit their predictive validity to assess injury risk across a population of workers who we know to be inherently variable in terms of movement. The objective of this study was to evaluate the prospective benefit of considering movement variability in ergonomic risk assessment. To address this objective a proof-of-principle model was developed to evaluate the variance in movement and corresponding predicted peak low back compression loads during floor-to-waist height lifting as a function of variance in personal factors (i.e. expertise, height, body mass, sex, etc.). The developed model was based on experimental data (n = 72), and was sufficient to predict mean compressive forces within ±50 N. A use-case analysis revealed that predicted peak compression loads had a range of up to 5000 N across simulated male and female populations due the movement variability within a given pre-defined anthropometry. This range of predicted peak low back compression loads supports the importance of considering variability in ergonomic assessment as this variance would not be captured in existing deterministic risk assessment models.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".