Assessing Effectiveness of Passive Exoskeletons and Tool Selection on Ergonomic Safety in Manhole Cover Removal
Bibliographic record
Abstract
Manual material handling, a common practice in various industries, often involves moving or lifting heavy objects, placing significant physical strain on workers, especially in the lower back. A prime example is manhole cover removal, which typically requires handling heavy weights, potentially leading to lower back muscle strain. This study investigates the effectiveness of a passive exoskeleton in reducing ergonomic risks during manhole cover removal. Twenty able-bodied workers participated, performing the task with and without extractor tools in the field. Techniques such as surface electromyography and inertial measurement units were employed to measure muscle activity and body posture using the Rapid Entire Body Assessment (REBA). This study compared muscle activities and REBA scores under different conditions: manually lifting covers, using an in-house lever tool, and using a sledgehammer and a pick bar tool named Jake, both with and without an exoskeleton. Results revealed that the in-house Lever tool was the safest and most efficient method, resulting in the lowest muscle activities and REBA scores, regardless of exoskeleton use. Interestingly, the exoskeleton significantly reduced muscle strain when using the Jake tool. These findings indicate that while the Lever tool is optimal for this task, passive exoskeletons can effectively lower ergonomic risks associated with more physically demanding tools.
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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 | 0.001 | 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".