DYNAMIC MODELING AND EVALUATION OF A ROBOTIC EXOSKELETON FOR UPPER-LIMB REHABILITATION
Bibliographic record
Abstract
Proper functioning of the shoulder, elbow, and wrist movements play a vital role in the performance of essential daily activities. To assist physically disabled people with impaired upper-limb function, we have been developing an exoskeleton robot (ExoRob) to rehabilitate and to ease upper limb motion. The proposed ExoRob will be comprised of seven degrees of freedom (DOFs) to enable natural movements of the human upper-limb. This paper focuses on the kinematic and dynamic modeling of the proposed ExoRob that corresponds to human upper-limbs. For this purpose, a nonlinear computed torque control technique was employed. In simulations, trajectory tracking corresponding to typical rehabilitation exercises were carried out to evaluate the performances of the developed model and controller. For the experimental part, only 3DOFs (elbow, wrist flexion/extension, wrist abduction/adduction) were considered. Simulated and experimental results show that the controller was able to maneuver the proposed ExoRob efficiently in order to track the desired trajectories, which in this case consisted in passive arm movements. Such movements are widely used in therapy and were performed efficiently with the developed ExoRob and the controller.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| 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".