Design of a Magnetorheological Damper-Based Haptic Interface for Rehabilitation Applications
Bibliographic record
Abstract
This article presents a novel haptic interface for rehabilitation purposes using MR-dampers. In the rehabilitation, patients are required to do certain exercises to train damaged muscles. Specialized devices are required to ensure patients will do the exercise accurately. Typical devices that are used for this application are difficult to program and may cause damage by applying excessive force to human body. The haptic device that is designed in this article will address the issues by employing MR-dampers and a user-friendly programming methodology. The concept of Resistive-Map generation is introduced as main strategy for activating MR-dampers and restricting the motion to the regions determined by the therapist. To simulate the performance of the system, an accurate model of MR-damper is obtained and validated experimentally. To test the performance of the proposed MR-based haptic device, the resistance-maps are first generated. MR-dampers are activated according to the positions of the MR-dampers in the resistance-map. The system is also simulated in MATLAB ® / SimMechanics. The experimental and simulation results are in good agreement. The promising results of the proposed haptic interface make it a potential candidate for rehabilitation applications. Patients will be able to take the device home and the physiotherapists can online programme the exercises and monitor the performance of patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".