Digital Resistance-Map Generation for a Magnetorheological Damper Based Platform for Rehabilitation Applications
Bibliographic record
Abstract
This paper introduces a methodology for generating digital resistance-map that can be utilized in an MR-Damper based robotic rehabilitation. Typically, in rehabilitation procedures, patients are getting involved in the recovery process of gradually training weak and damaged muscles by constraining motion in repetitive exercises. The whole purpose of robotic rehabilitation is to restrict body organ motion to the one prescribed by the therapist at the initial steps of treatment to avoid further damages to other weak muscles while focusing on recovering a particular muscle. MR-Dampers are semi-active actuators that can potentially be employed for this application. These dampers can be activated to produce high resistance to motion, and a platform that contains sufficient number of them can be manipulated to create regions of different resistance against motion. To apply this to the robotic rehabilitation, the motion recommended by the therapist should be converted to the resistance-maps that can be used by MR-Damper for implementation. To accomplish that, procedure of generating the digital resistance map is introduced and several digital resistance-maps are created. An MR-damper control methodology is also developed to activate the dampers. This controller relies on the accurate modeling of the MR-Damper. Bouc-Wen model is used for MR-Damper modeling. A 3-D platform containing three linear MR-Dampers is modeled using SimMechanics. 1-D and 2-D models are used to develop the idea and build up 3-D model. Several simulations are carried out to investigate the performance of the systems in generating the prescribed digital resistance-maps. The promising results of the simulations indicate that the method can be adopted for robotic rehabilitation purposes.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".