Evaluating an IoT under-mattress sensor mat for detecting anomalies in sleep parameters: A pilot study
Bibliographic record
Abstract
Sleep is equally important as a healthy diet, and exercising and not getting adequate sleep can lead to being less physically active during the day. Still, sleep is underrated by many people. Measuring sleep at home using conventional methods is not practical, given the complexity of equipment, so several user-friendly sleep monitoring devices are continually being developed. This paper aims to evaluate the effectiveness of an IoT under-mattress sensor mat for identifying anomalies in sleep parameters under a real-life scenario out of the lab. The sensor mat embodies a microbend fiber optic sensor that is sensitive enough to capture the mechanical activities caused by the heart. Five older adults participated in the study, and raw sensor data were gathered over several weeks. Limits of agreement and boxplots were employed to identify anomalies in three sleep parameters, i.e., wake-up time, bedtime, and time in bed. Also, a sleep history metric was applied to captures an aggregate measure of sleep behavior. Albeit using a single-channel sensor, sleep anomalies were detected and verified by the participants' caregivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".