Estimating in-home walking speed distributions for unobtrusive detection of mild cognitive impairment in older adults
Bibliographic record
Abstract
Timely recognition of cognitive impairment such as Alzheimer's disease is of great significance. Many smart systems, developed to continuously monitor older adults' health and cognition, use a number of predefined measures associated with the older adults' activity in their homes. However, this approach has been demonstrated to focus on idiosyncratic nuances of the individual subjects, and thus could potentially not perform as well when tested on new subjects. In this paper, we address this problem by building proper statistical models of older adults' in-home walking speed. Using the data pertaining to 15 older adults monitored for an average period of 3 years, we used linear regression with a Gaussian likelihood to model the subjects' in-home walking speed, and we used dynamic time warping to demonstrate significant difference between the walking speed distributions of the subjects when cognitively intact and when having mild cognitive impairment (MCI). Using a simple thresholding approach of the dynamic time warping costs, we were able to detect MCI in older adults with areas under the ROC curve and the precision-recall curve of 0.906 and 0.790, respectively, using a time frame of 12 weeks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".