Bibliographic record
Abstract
Robotic assistive technologies are increasingly used to enhance the physical rehabilitation of patients who have suffered disorders such as strokes. Not only does it make the lives of disabled and elderly patients easier, but it also improves their body functionalities. Robotic assistive technologies offer people a second chance to overcome challenges that come with their disability. The objective of the thesis is to design, prototype and evaluate a 3 Degrees of Freedom (DOF) pneumatic manipulandum for wrist rehabilitation that is capable of accommodating to wrist motions (ulnar deviation, radial deviation, flexion or extension). Since the wrist is the most mobile part of the hand, its post-stroke rehabilitation is difficult. In order to accommodate the wrist motion, 3 DOF are needed. 2 DOF are needed for the horizontal motion and another DOF to allow the manipulandum to move up and down with the wrist. Each DOF is actuated by one pneumatic actuator. The design is prototyped using a 3D printer. The workspace and the required force are analyzed and calculated based on the kinematics of the manipulandum. The pneumatic actuators that were chosen are available in non-magnetic material, which means they are compatible with Functional Magnetic Resonance Imaging (fMRI-compatible). The manipulandum is connected to a Neuro Function Evaluation (NFE) game which is used in the Rehabilitation Centre in Winnipeg. While running the game, the manipulandum is tested and evaluated in assistive and resistive modes. The performance of the manipulandum is analyzed using two methods: image processing and file streaming. The image processing method determines the location of the ball and the location of the paddle of the NFE game in the screen by taking screenshots, while the file streaming method is used to obtain those two locations from the code of the game itself.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| 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) | 0.004 | 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".