Multibody dynamics and optimal control for optimizing spinal exoskeleton design and support
Bibliographic record
Abstract
Abstract In the industrial work environment, spinal exoskeletons can assist workers with heavy lifting tasks by reducing the needed muscle activity. However, the requirements for the design and control of such an exoskeleton to optimally support users with different body builds and movement styles are still open research questions. Thus, extensive testing on the human body is needed, requiring a lot of different sophisticated prototypes that subjects can wear for several hours. To facilitate this development process, we use multibody dynamics combined with optimal control to optimize the support profile of an existing prototype and evaluate a new design concept (DC) that includes motors at the hip joint. A dynamic model of the prototype was developed, including its passive elements with torque generation that accounts for potential misalignment. The human-robot interaction was simulated and optimized in an all-at-once approach. The parameters that describe the characteristics of the passive elements (including beam radius, spring pretension, length of the lever arm, radius of profile) and, in the case of DC, the torque profiles of the motors were optimized. Limits on interaction forces ensured that the exoskeleton remains comfortable to wear. Simulations without the exoskeleton allowed comparing the user’s actuation concerning joint moment and muscle activation. Our results agree well with experimental data using the prototype, making it a useful tool to optimize exoskeleton design and support and evaluate the effect of different actuation systems, mass distributions, and comfort requirements.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".