Adaptive Impedance Control in Bilateral Telerehabilitation with Robotic Exoskeletons
Bibliographic record
Abstract
Telerehabilitation with Robotic Exoskeletons is an emerging technology aimed at assisting to restore patients' mobility using a master and slave robotic system. Some of the main challenges for achieving good tracking performance, stability and transparency in telerehabilitation are nonlinearities, uncertain and time-varying parameters in the robot and human models, and communication delays. Additionally, a paramount challenge for this technology is ensuring safe and compliant interaction between the robots and the human operators. This paper presents a novel control approach utilized during unilateral and bilateral teleoperation which address these challenges. An Adaptive Impedance Controller is designed using Lyapunov-based methods for the master exoskeleton while a Proportional-Derivative Impedance Controller is implemented on the slave exoskeleton. Subsequently, a torque limiter technique was implemented on the master side to ensure stability in the presence of time delays. The advantages of these controllers are that they address unknown dynamics, incorporate designed impedance response for rehabilitation applications, and are simple to implement. Simulations for two two-degree-of-freedom robotic exoskeletons are provided to demonstrate the effectiveness of these methods in both passive and assistive telerehabilitation modes, and with time delays.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".