How Long Is Long Enough? Evaluating Sampling Durations for Low Back EMG Assessment
Bibliographic record
Abstract
Few ergonomic measurement tools explicitly state when and how to sample exposures. Traditional ergonomic sampling has used short, task-based or worst-case measurements, but these may misrepresent exposures, since they neglect the temporal variations throughout the workday. Understanding the representativeness of data from shorter measurement durations compared with full-shift measurements allows for optimization of measurements resources. This study compared a variety of low back electromyography (EMG) exposure metrics measured over a full-shift with the same metrics sampled over shorter durations to identify whether shorter durations provide representative measures of exposure. Portable EMG devices were used to measure low back EMG for 138 full work shifts in a range of jobs in heavy industry. Using a random start time, each full shift of data was resampled for 4 hr, 2 hr, 1 hr, 10 min, and 2 min. Exposure metrics from each duration were compared with the full shift using absolute and percent error, bias, and limits of agreement. Comparisons between one full shift and two full shifts were made for the subset of 35 workers with two measured workdays. Compared with full-shift data, bias is very low at all sampling durations. However, as sampling durations decreased from a full-shift to a few min, the absolute error, percentage error, and limits of agreement for exposure estimates show more deviation from full-day estimates. Estimates of mean and 90th percentile exposure averaged 8% error for 4-hr durations and 14% error for 2-hr durations. The errors for 4-and 2-hr measurement durations may be acceptable for many applications, particularly if the trade-off is measuring more subjects. Sampling durations of 1 hr or less seem likely to produce very large errors over all exposure metrics, particularly for the range and peak exposures. Depending on the purpose of measurement and the detail required, 4 hr or even 2 hr appears to be long enough to reasonably estimate full-shift exposure.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".