Improving Freezing of Gait Detection and Prediction Using ML and Transformers
Bibliographic record
Abstract
Detecting Freezing of Gait (FOG) is crucial for Parkinson's disease (PD). Several research works have been published on FOG detection and prediction using machine learning (ML) and limited accelerometer data. FOG data collection is challenging and generally conducted on a limited number of persons as freezing occurs randomly, unlike walking which is a recurrent activity. This makes directly applying ML inefficient. In this paper, to improve previously published results, we apply three different ML algorithms: Linear Discriminant Analysis (LDA), Extreme Gradient Boosting (XGB), and Extra Trees (ET), in addition to a Transformer model to detect and predict FOG. This is achieved by modelling the publicly available DAPHNet dataset, which contains accelerometer readings from three wearable sensors. The data quality is first improved by using data augmentation, data balancing, and feature extraction. Our results show that accuracy (ACC), along with other metrics like sensitivity (SEN), specificity (SPE), and F1-score (F1) is important to quantify the performance of our results. From FOG detection results, we can see that XGB and ET algorithms provide 100% ACC, and slightly lower performance, 99.96% with LDA. For FOG prediction, our results prove we can achieve 99.21%, 96.40%, 96.21%, and 94.70% ACC with LDA, XGB, ET, and Transformer, respectively. These results outperform previously published results on the same dataset.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".