An Integration of Health Tracking Sensor Applications and eLearning Environments for Cloud-Based Health Promotion Campaigns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rapidly evolving technologies like data analysis, smartphone and web-based applications, and the Internet of things have been increasingly used for healthy living, fitness and well-being. These technologies are being utilized by various research studies to reduce obesity. This paper demonstrates design and development of a dataflow protocol that integrates several applications. After registration of a user, activity, nutrition and other lifestyle data from participants are retrieved in a centralized cloud dedicated for health promotion. In addition, users are provided accounts in an e-Learning environment from which learning outcomes can be retrieved. Using the proposed system, health promotion campaigners have the ability to provide feedback to the participants using a dedicated messaging system. Participants authorize the system to use their activity data for the program participation. The implemented system and servicing protocol minimize personnel overhead of large-scale health promotion campaigns and are scalable to assist automated interventions, from automated data retrieval to automated messaging feedback. This paper describes end-to -end workflow of the proposed system. The case study tests are carried with Fitbit Flex2 activity trackers, Withings Scale, Verizon Android-based tablets, Moodle learning management system, and Articulate RISE for learning content development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.002 | 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