Detecting Parkinson’s Disease Using Machine Learning from Movement
Bibliographic record
Abstract
This study explored the potential of machine learning techniques, specifically decision trees and artificial neural networks (ANNs), to detect Parkinson's Disease (PD) using data collected from the mPower mobile iOS app. Released in 2016 by Sage Bionetworks, mPower enables individuals, both with and without PD, to assess their cognitive and physical abilities through various tasks related to memory, tapping, voice, and movement. The main focus of this study is on the walking task within the app's version 1.0 build 7. Participants were required to walk unassisted for approximately 20 steps in a straight line, followed by a 30-second period of standing still, and then returning with 20 steps. The smartphone's accelerometer and gyroscope captured three-dimensional (3D) rotation data (x, y, z) during these movements, with the device placed in the participant's pocket or bag. A convolutional neural network was applied to the movement dataset to assess confirmed PD cases, utilizing accelerometer and gyroscope readings during outward walking, return walking, and rest periods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".