Sleep Quality Index for Sensor Data in Eldercare Monitoring
Bibliographic record
Abstract
Early detection of health changes is important for the success of an aging population that prefers to live independently.Sleep is crucial for maintaining the cognitive and physical health of older adults.Poor sleep quality is common among the elderly with mild cognitive impairment (MCI), which is a transient state between healthy cognition and dementia.Monitoring sleep quality can provide valuable insights into the health trends of older adults, but current methods are uncomfortable and inconvenient.We instead use Ballistocardiography, an unobtrusive method of capturing time in bed, heart rate, respiration rate, and restlessness.We then propose a sleep quality index (SQI) that uses this data to assess the sleep health of older adults.This sleep index was evaluated on six residents with a total of 1165 days of sensor data.Our results demonstrate the effectiveness of the proposed method in capturing various health conditions, which are illustrated through detailed case studies.A comparative analysis further highlighted the relationship between health conditions and sleep quality, showing that residents with frequent health issues had a significantly lower SQI compared to healthier residents.This significant difference underscores the utility of the SQI as a sensitive measure for detecting and monitoring health-related changes in sleep quality among older adults.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".