Minimizing Misalignment and Frame Protrusion of Shoulder Exoskeleton via Optimization for Reducing Interaction Force and Minimizing Volume
Bibliographic record
Abstract
Although industrial shoulder exoskeletons have undergone rapid advancement, their acceptance by industrial workers is limited owing to the misalignment and interference between the exoskeletal frame and the wearer’s body and bulkiness of the frames. Several joint mechanisms have been developed to offset misalignments; however, none of the existing systems can simultaneously alleviate the interference and bulkiness problems. Furthermore, the reduction in the misalignments in terms of forces generated at the human–robot interface has not been experimentally verified. Therefore, in this study, design optimization was performed to address the various factors that limit the use of the existing industrial shoulder exoskeletons. Upper body motions were captured and converted into a target trajectory for the exoskeleton to follow. The optimal prismatic–revolute–revolute joint configuration was derived and used to manufacture a skeletal mock-up, which was used to perform experiments. The misalignments of the optimized configuration in the considered motions were 67% lower than those for the conventional joint configuration. Furthermore, the interaction forces were negligible (1.35 N), with a maximum reduction of 61.8% compared to those of conventional configurations.
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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) | 0.001 | 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".