A Multi-Sensor Monitoring System of Human Physiology and Daily Activities
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To present the design and pilot test results of a continuous multi-sensor monitoring system of real-world physiological conditions and daily life (activities, travel, exercise, and food consumption), culminating in a Web-based graphical decision-support interface. MATERIALS AND METHODS: The system includes a set of wearable sensors wirelessly connected to a "smartphone" with a continuously running software application that compresses and transmits the data to a central server. Sensors include a Global Positioning System (GPS) receiver, electrocardiogram (ECG), three-axis accelerometer, and continuous blood glucose monitor. A food/medicine diary and prompted recall activity diary were also used. The pilot test involved 40 type 2 diabetic patients monitored over a 72-h period. RESULTS: All but three subjects were successfully monitored for the full study period. Smartphones proved to be an effective hub for managing multiple streams of data but required attention to data compression and battery consumption issues. ECG, accelerometer, and blood glucose devices performed adequately as long as subjects wore them. GPS tracking for a full day was feasible, although significant efforts are needed to impute missing data. Activity detection algorithms were successful in identifying activities and trip modes but could benefit by incorporating accelerometer data. The prompted recall diary was an effective tool for augmenting algorithm results, although subjects reported some difficulties with it. The food and medicine diary was completed fully, although end times and medicine dosages were occasionally missing. CONCLUSIONS: The unique combination of sensors holds promise for increasing accuracy and reducing burden associated with collecting individual-level activity and physiological data under real-world conditions, but significant data processing issues remain. Such data will provide new opportunities to explore the impacts of human geography and daily lifestyle on health at a fine spatial/temporal scale.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it