Biomechanical effects of soft and rigid passive back occupational exoskeletons during load-carrying and static trunk bending tasks in the aeronautics industry
Bibliographic record
Abstract
Abstract The manufacturing industry, notably the aeronautics sector, involves tasks presenting risks of low back pain. One of the preventive strategies could be the use of passive back exoskeletons, which have demonstrated benefits during activities involving trunk bending. This study aims to evaluate the effects of four passive back exoskeletons on trunk neuromuscular activity, kinematics, and perceived discomfort during polishing tasks simulated in a laboratory setting. Nineteen participants performed four tasks (two static bending tasks and two load-carrying tasks) without and with two soft (CORFOR and BionicBack) and two rigid (BackX and Laevo FLEX) exoskeletons. The results showed varying effects depending on the tested exoskeleton model, beyond the distinction between rigid and soft designs. Reductions in lumbar erector spinae (LES) neuromuscular activity were observed with Laevo FLEX and CORFOR during static tasks compared to the condition without exoskeleton (8–18%; p < .05). However, reductions in LES muscle activity were not significant during load carrying. Biceps femoris neuromuscular activity was significantly lower in the four tasks when using the Laevo FLEX, with reductions ranging from 8 to 17% ( p < .01). The two rigid exoskeletons decreased perceived back discomfort across all tasks ( p < .05). Finally, the BionicBack exoskeleton significantly altered participants’ kinematics across all four tasks, reducing both trunk range of motion and average flexion (p < .05). The Laevo FLEX exoskeleton was the only one to significantly reduce both neuromuscular activity and perceived back discomfort, while causing no adverse effects, appearing advantageous when polishing in the aeronautical industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".