Feasibility of quantifying the physical exposure of materials handlers in the workplace with magnetic and inertial measurement units
Bibliographic record
Abstract
Handling tasks can expose workers to risk factors. The objective was to describe the feasibility of using magnetic and inertial measurement units (MIMUs) to quantify the physical exposure of materials handlers in the workplace. Full-body kinematics were obtained with MIMUs on 10 handlers gathering products ordered by retailers with a pallet truck. An observer classified the visual difference (VD) of segment orientation between a MIMUs avatar and video recordings in three categories (none, minor and major) for each product transfer. The feet, arms, shoulders and head were considered similar for ≥97% of observations. The trunk segment obtained the most differences with 9% of minor VD and 5% of major VD, which were related to the duration of the magnetic disturbances of the MIMUs. Estimating parameters of the physical exposure of handlers in the workplace is feasible with kinematics and an order list, but visual verification remains important for scientific rigour.Practitioner Summary: The feasibility of measuring physical exposure with magnetic and inertial measurement units was evaluated on materials handlers in the workplace. Visual observation of the postures indicated that most of the data is considered acceptable. Magnetic disturbances can increase the measurement error, so data must be verified to ensure validity.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".