Generating Models of Human Gait in Patients with Parkinson’s Disease using Genetic Programming
Bibliographic record
Abstract
Parkinson’s disease is an extremely debilitating condition where the brain is not producing enough dopamine to accurately coordinate movement. One symptom of Parkinson’s disease, freezing of gait, prevents the affected person from either starting to walk or continuing walking. We use a dataset that provides time-series data of volunteers’ gait while performing four different tasks of varying complexity. Symbolic regression through genetic programming is applied to the dataset to create models of the gait of volunteers with and without Parkinson’s disease, including those who may be experiencing freezing of gait, while also factoring in their medication status (ON or OFF). Comparing the gait of volunteers in the different groups, the methodology produced models that were most accurate for the group of PD patients with freezing of gait when off their medication applied to other volunteers in the same group. It was also found that for some volunteers it was not possible to distinguish between ON and OFF medication states, providing a possible indication of issues with their medication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".