Effects of standing and sitting postures on an isoinertial pulling task
Bibliographic record
Abstract
This study examined the effects that specific postures had on the neuromuscular activities of four muscles during an isoinertial pulling task. Ten male subjects volunteered to execute one-handed pulls at 15% of their body mass, at a frequency of 12 pulls per minute, for a duration of 12.5 minutes in both a standing and sitting posture. Electromyographical (EMG) activities of the posterior deltoid, trapezius, latissimus dorsi and erector spinae ( L 4/ L 5 level) were recorded by a portable data collection system using bipolar surface electrode configurations. Collected EMG data were subsequently analysed for differences in magnitude and rate of fatigue between conditions. Heart rates were also recorded for both conditions. Cardiovascular and neurophysiological responses provided no evidence of fatigue due to the execution of the task, suggesting that these workload and postures would be suitable for industrial applications. Analyses revealed significant differences between conditions in the level of activation for all muscles except the trapezius, suggesting that muscle recruitment is highly influenced by posture during common pulling activities. These findings support a conclusion that an operator performing repetitive submaximal (i.e. less than 15% of a subject's absolute body mass) pulling tasks would benefit from a workstation designed to accommodate standing and sitting postures in order to vary the manner in which agonist muscles are recruited.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".