Generating Models of Human Gait in Patients with Parkinson’s Disease
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. It usually begins in the advanced stages of the disease. The primary medication for Parkinson’s disease, Levodopa, is only partially effective for the treatment of freezing of gait. The dataset studied in this thesis provides time-series gait data of individuals’ gait while performing four different tasks, each having increased complexity over the previous ones. This thesis looks at a time-series gait dataset and performs symbolic regression through genetic programming on that dataset to predict fall likelihood and to create models of the gait of people with and without Parkinson’s disease including people who may be experiencing freezing of gait while factoring in their medication status (ON or OFF). The fall prediction experiment suggests that the GP models can predict the likelihood of falling based on the individual’s gait. The models provide insights into how Parkinson’s disease and freezing of gait impact gait patterns in people who have the disease vs. those who do not and enables us to compare the gait of individuals in different groups. It was found that, as expected, gait was similar within groups and different between groups. We also found that for some individuals it was not possible to distinguish between ON and OFF medication states. Future work might include determining the best models for each individual or group, attempting to find a model that accurately represents the individual or group rather than the individual trials.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".