The effects of a moving environment on postural control and performance during manual materials handling, visual tracking and arithmetic tasks
Bibliographic record
Abstract
The purpose of this study was to evaluate the performance of cognitive tasks and manual materials handling in a moving environment. In particular we were interested in how task performance, postural control and lower limb muscle activation changed when tasks were performed in motion compared to no motion conditions. The motion trials were performed on a MOOG 2000E that created a 5-degrees of freedom simulated environment. The tasks examined were a lifting task, a mental arithmetic task and a visual tracking task. Results of this experiment indicated that two outcome measures of a visual tracking task (time to task completion and performance errors) were negatively affected by motion, while arithmetic task performance was unaffected. Additionally, postural control was not affected by the presence of motion in the two cognitive tasks. Lifting was the only task where postural control appeared to be negatively affected as participants exhibited significant increases in lower limb muscle activation and non-significant increases in number of steps taken. The significant increase in time to completion and errors suggest that workers doing these type of tasks in an offshore environment may be more prone to committing human factors errors. Furthermore, the results suggest that the risk of falls and injury due to loss of balance may be highest in workers regularly performing lifting tasks as this was the only task where task performance in a moving environment negatively impacted postural control. These findings were attributed to greater demands placed on the postural control system when lifting during the motion condition. This study provides ergonomists with a resource they can use to better appreciate the risks associated with performance of job related tasks in a moving environment.
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 | 0.000 | 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) | 0.001 | 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".