Ability of a passive back support exoskeleton to mitigate fatigue related adaptations in a complex repetitive lifting task
Bibliographic record
Abstract
As work related musculoskeletal disorders (WRMSDs) increase in prevalence, it is important to not only understand the mechanisms underpinning WRMSDs but also investigate ways to mitigate them in the workplace. Exoskeletons are an emerging technology which can reduce the physical demands required from the worker to perform lifting tasks. Previous research has primarily studied the effectiveness of back-support exoskeletons in constrained, flexion/extension tasks. The purpose of this study was to evaluate the effect of a passive back-support exoskeleton in a complex, multiplanar repetitive manual materials handling (MMH) task. Participants (n = 14) completed a multi-planar lifting, transferring, and lowering task for sixty minutes with and without wearing a passive back-support exoskeleton (HeroWear Apex 2). Full body kinematics as well as trunk and shoulder surface electromyography were collected for the entire trial. The back-support exoskeleton significantly decreased ratings of perceived exertion (p < 0.0001) Participants exhibited faster task completion times in the exoskeleton condition (p = 0.0095). Both movement coordination and coordination variability differed between conditions with increased shoulder-lumbar variability (p = 0.0466 - <.0001) and decreased thorax-pelvis variability (p = 0.0281) in the exoskeleton condition. Additionally, the back-support exoskeleton significantly reduced lumbar erector spinae muscle activity asymmetrically (p < 0.0001). While these findings generally support the idea that a passive back-support exoskeleton is effective in altering indicators of fatigue which can lead to injury, more research is needed to study different exoskeleton assistance levels and different types of exoskeletons before recommending their use across a diverse range of occupational settings.
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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 | 0.001 |
| 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) | 0.002 | 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".