A Smartphone-based Wellness Assessment Using Mobile Sensors
Bibliographic record
Abstract
Developments in the Internet of Things (IoT) in recent years has allowed for wireless sensor data to be collected and communicated with more ease than ever. This facility of data acquisition has opened possibilities in a large variety of fields, including potential for a significant impact in health care. This paper introduces a framework using IoT sensors to examine correlations between environmental conditions and overall wellness. The proposed system uses a SimpleLink Bluetooth SensorTag and a mobile application to collect environmental data from a subject's surroundings on a daily basis. The participants also complete daily surveys, which include modified questions from the Pittsburgh Sleep Quality Index (PSQI), the Perceived Stress Scale (PSS), and the Kessler Psychological Distress Scale (K10). Once any correlations between environmental variables and overall wellness have been determined, it should be possible to use this technology to assess and predict one's wellness using environmental data.
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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) | 0.004 | 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 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".