Real-time Location Data to Classify Degree of Cognitive Impariment and Motor Agitation
Bibliographic record
Abstract
Real Time Location Systems can produce spatiotemporal data from people with dementia that might provide information about their cognition and health status. The first study explored a novel method to classify the severity of cognitive impairment (‘No-CI’, ‘Mild-Moderate CI’, ‘Severe CI’) among residents of a care unit using walking path images. The RTLS data was distributed into windows of various durations and transformed into images used in a Convolutional Neural Network that achieved a top accuracy of 87.38%. Class Activation Mapping was used to consolidate an objective score that can reliably distinguish between each class. The second study utilized features of spatiotemporal data with single-instance and multi-instance learning techniques in generalized and personalized models to detect low and high motor agitation. The top performing model achieved 0.71 with the Receiver Operator Characteristic in 5-fold cross validation with an indication that personalized models may require more data for improvement.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".