Automated Physical Performance Battery as a Digital Marker for Alzheimer’s Disease and Mild Cognitive Impairment
Bibliographic record
Abstract
Historically, screening for incidence of AD-related MCI or conversion from MCI to AD dementia has relied on cognitive, activities of daily living, and brain imaging measures. Limitations of this diagnostic approach include dependency on education and language, time-consuming and costly measures, and long-term monitoring. Emerging studies suggest that non-tremor motor dysfunction in dementias is known to be highly associated with AD biomarkers, with signs of cognitive decline visible in gait and hand movement at various stages of the illness. With the evidence that gait and physical disturbances are early predictors of cognitive impairment and that their trajectories could readily be tracked, we utilize recent advances in computer vision (CV) to quantify mobility in a data-driven fashion from the video-recorded 5-minute Short Performance Physical Battery (SPPB) tests. We use the data collected at Stanford AD Research Center and show that our CV methods can automatically reduce videos to body markers (human skeleton tracked through time) and extract several features (such as gait speed, mean torso inclination angle, double support time, gait-summary score, etc.) and finally turn those into clinical SPPB test scores. Our initial data observed a significant difference between healthy controls (HC) and the two MCI and AD groups for the repeated chair stand test score. Similarly, an inverse correlation between the MoCa cognitive test score and the gait speed is observed. At the end of the talk, I will also discuss how this CV method for mobility can be used for detecting behavioral changes in animal AD models and implications for future human AD research.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".