Robot assisted rehabilitation for elbow and forearm movements
Bibliographic record
Abstract
The movements of the shoulder, elbow, and wrist play a vital role in the performance of essential daily activities. We therefore have developed a 2DOF exoskeleton robot ( ExoRob ) to rehabilitate the elbow and forearm movements of physically disabled individuals with impaired upper-limb function. The proposed ExoRob is supposed to be worn on the lateral side of forearm in order to provide naturalistic range movements of elbow (flexion/extension) and forearm (pronation/supination) motions. This paper focuses on the modelling, design (electrical and mechanical components), development, and control of the proposed ExoRob . The kinematic model of ExoRob has been developed based on modified Denavit-Hartenberg notations. Non-linear modified computed torque control technique is employed to control the proposed ExoRob , where trajectories (i.e., pre-programmed trajectories recommended by therapist/clinician) tracking corresponding to typical rehabilitation (passive) exercises has been carried out to evaluate the performances of the developed ExoRob and controller. Furthermore, experiments were carried out with the master exoskeleton arm [ mExoArm , an upper-limb prototype 7DOF (lower scaled) exoskeleton arm] where subjects (robot users) or experimenter operate the mExoArm (like a joystick) to manoeuvre the proposed ExoRob to provide passive rehabilitation. Experimental results show that the controller is able to manoeuvre the ExoRob efficiently to track the desired trajectories. Such movements are widely used in rehabilitation and have been performed efficiently with the developed ExoRob and the controller.
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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) | 0.003 | 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".