Design of a Biomedical Device to Reduce Anxiety Experienced by Patients Diagnosed with Parkinson’s Disease
Bibliographic record
Abstract
Parkinson’s disease (PD) is a neurodegenerative disease that occurs due to loss of nerve cells that produce dopamine in the brain, affecting approximately 4 million people worldwide. PD patients often feel an increase in anxiety levels daily. While there are medications/exercises to help relieve anxiety, there are limited methods to reduce anxiety without the help of a caretaker. As a result, MEDIC Foundation, a non-profit organization in British Columbia, Canada, is designing an automated system that consists of a wristband and an application which uses vi-bration therapy to help reduce anxiety of PD patients. Literature reviews were conducted to document the project’s needs. Phase I of the project focused on de-veloping a prototype for the application and phase II on developing the wrist-band. The team developed prototypes of a wristband that automatically applies vibration near the median nerve as the heart rate variability (HRV) deviates away from the normal threshold of the user, and an application that displays real-time heart rate variability signals as well as provides for relaxation. The development of the prototype is still in early progress. By creating this automated system, we aim to provide a solution to senior PD patients to relieve anxiety independently.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".