Compliant control for wearable exoskeleton robot based on human inverse kinematics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rehabilitation robots are a new technology dedicated to the physiotherapy and assistance motion and has aroused great interest in the scientific community. These kinds of robots have shown a high potential in limiting the patient’s disability, increasing its functional movements and helping him/her in daily living activities. This technology is still an emerging area and suffers from many challenges like compliance control and human–robot collaboration. The main challenge addressed in this research is to ensure that the exoskeleton robot provides an appropriate compliance control that allows it to interact perfectly with humans. This article investigates a new compliant control based on a second-order sliding mode with adaptive-gain incorporating time delay estimation. The control uses human inverse kinematics to complete active rehabilitation protocols for an exoskeleton robot with unknown dynamics and unforeseen disturbances. The stability analysis is formulated and demonstrated based on Lyapunov function. An experimental physiotherapy session with three healthy subjects was set up to test the effectiveness of the proposed control, using virtual reality environment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it