Design and Implementation of a Novel Rehabilitation Robot for Acute Stroke Patients
Bibliographic record
Abstract
Stroke is a prevalent cerebrovascular disease which leads to neurological deficits, and is a major cause of disability in adults.Stroke victims often lose motor control, affecting their ability to perform activities of daily life such as walking and grasping.Rehabilitation is used to help restore lost abilities through intensive and repetitive exercises with the assistance of a therapist.Robots have been introduced to help therapists administer therapy by replicating the role of physically assisting the patient.There is a gap in the literature regarding rehabilitation robots for bed-bound, acute stroke patients -this motivated the design and creation of the novel rehabilitation device presented in this thesis.The device builds off of previous work with the Virtual Gait Rehabilitation Robot (ViGRR), seeking to take the fundamental concepts and apply them to acute, bed-bound stroke rehabilitation.Observational fieldwork on the stroke ward at a local hospital informed the initial design, a linear 1-DOF robot targeting the knee flexion/extension exercise typical to traditional bed-bound rehabilitation.The device sits on the bed, under the leg, with the patient's foot resting on a footplate.An admittance controller was developed to apply assistive or resistive forces to the patient's leg.Modular real-time software was created to handle the controller, communication, and data logging.Three games were created which are used to make the exercise more engaging, along with haptic feedback control which can render virtual forces to the patient through the robot.The games run on a user interface, which is also used to set up rehabilitation sessions by selecting assistance levels, resistance level, and type of game.A series of experiments were run, including safety tests and functional tests which confirm that the controller and software are functioning correctly.We conducted an experiment on healthy subjects, which i
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".