Nonlinear model predictive control of an upper extremity rehabilitation robot using a two-dimensional human-robot interaction model
Bibliographic record
Abstract
Stroke rehabilitation technologies have focused on reducing treatment cost while improving effectiveness. Rehabilitation robots are generally developed for home and clinical usage to: 1) deliver repetitive practice to post-stroke patients, 2) minimize therapist interventions, and 3) increase the number of patients per therapist, thereby decreasing the associated cost. The control of rehabilitation robots is often limited to black-or gray-box approaches; thus, safety issues regarding the human-robot interaction are not easily considered. To overcome this issue, controllers working with physics-based models gain more importance. In this study, we have developed an efficient two dimensional (2D) human-robot interaction model to implement a model-based controller on a planar end-effector-type rehabilitation robot. The developed model was used within a nonlinear model predictive control (NMPC) structure to control the rehabilitation robot. The GPOPS-II optimal control package was used to implement the proposed NMPC structure. The controller performance was evaluated by simulating the human-robot rehabilitation system, modeled in MapleSim®. In this system, a musculoskeletal model of the arm interacting with the robot is used to predict movement and muscle activation patterns, which are used by the controller to provide optimal assistance to the patient. In simulations, the controller achieved desired performance and predicted muscular activities of the dysfunctional subject with a good accuracy. In our future work, a structure exploiting the NMPC framework will be developed for the real-time control of the rehabilitation robot.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".