Multibody dynamics and optimal control for optimizing spinal exoskeleton design and support
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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) | 0.000 | 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 it