Hybrid Position and Force Control for Upper Limb Rehabilitation of Stroke Patient
Bibliographic record
Abstract
This paper presents hybrid position and force control (HPFC) methodology for position and as well as force tracking for upper limb rehabilitation of stroke patients. Stroke is a leading cause of disability in humans. Traditional rehabilitative therapies help regain motor function and ameliorate impairment, but they depend on the therapist’s experience and require many therapists, which is cost-prohibitive. Most robotic tasks with high severity such as rehabilitation, demands an effective force as well as position control scheme to ensure the safe physical contact between the robot and its environment in this case the patient. Some rehabilitation robots have been developed to help stroke survivors recover motor function. In past robots used for upper-limb rehabilitation employed general control schemes such as proportional integral derivative. To overcome the problem of force tracking in rehabilitation, robots require modern control techniques. To mimic the human upper-limb, and universality of application, an end-effector based robot is used for this study. To ensure the convergence of position and force errors to zero extensive simulations are performed. Two of assistant modes passive and active assistive rehabilitation are considered. Most common rehabilitation trajectories horizontal reaching, and vertical reaching are selected as robot’s motion for both passive and active assistive activities. The mathematical model of robots’ kinematics, dynamics, alongside the proposed control scheme has been discussed in detail.
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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 | 0.000 |
| 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) | 0.002 | 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".