Upper-Limb Rehabilitation of Patients with Neuromotor Deficits Using Impedance-Based Control of a 6-DOF Robot
Bibliographic record
Abstract
This paper presents a study on the use of impedance-based control of a 6-degree-of-freedom robot for upper-limb rehabilitation of patients with neuromotor deficits. The control strategy is based on impedance and does not require external force sensors at the end-effector for implementation. The experimental setup involved using the control algorithm to move the robot to a desired position, follow a desired trajectory while being moved out of the trajectory by the user, and reproduce three different rehabilitation exercises (passive, isometric, and ADL). The results suggest that the parameters of the control strategy can be adjusted to set the robot's compliance and support force according to the patient's needs. Ultimately, the study concluded that the proposed control strategy can serve as a foundation for rehabilitation robots, which could potentially improve access to continued upper-limb rehabilitation for stroke patients and reduce the number of healthcare professionals required per patient.Clinical Relevance- This paper proposes a human-robot interaction control scheme to be used in a robotic rehabilitation tool, with the goal of assisting both clinicians and patients during the rehabilitation exercises and ultimately reducing the load on healthcare system. This platform can be potentially used in telemedicine solutions for upper-limb rehabilitation of a wide range of patients with neuromotor deficits such as stroke.
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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".