Review of Control Methods for Upper Limb Telerehabilitation With Robotic Exoskeletons
Bibliographic record
Abstract
Given the escalating unmet demand for physical rehabilitation due to the growing global aging population and the effects of the coronavirus COVID-19 including increased incidents of stroke, hospital bed shortages, and clinics closures, robotic telerehabilitation is an emerging, timely, and crucial technology. Rehabilitating the upper limbs of affected patients is of upmost importance for restoring physical function and lighten the societal burden due to disabilities. So far, the majority of the research in robotic telerehabilitation for upper limbs has been performed with end-effector-type assistive robots; however, the use of robotic exoskeletons has significant and distinctive benefits. Although there are surveys written about control methods for upper limb robotic exoskeletons and other surveys written about bilateral teleoperation control methods, there are no surveys written specifically about telerehabilitation control methods for upper limbs using robotic exoskeletons. As a result, this article reviews the state-of-the-art control strategies including various advanced linear and nonlinear control approaches for upper limb rehabilitation robotic exoskeletons, bilateral teleoperation, and several state-of-the-art telerehabilitation applications with upper limb robotic exoskeletons. The benefits, drawbacks, challenges, and future directions of existing methodologies are extensively discussed. This article offers a comprehensive overview and insight for new researchers in the area of telerehabilitation robotic exoskeletons.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".