IoT-Based Smart Health Monitoring System: Investigating the Role of Temperature, Blood Pressure and Sleep Data in Chronic Disease Management
Bibliographic record
Abstract
The Internet of Things (IoT) has become increasingly integral in healthcare, enhancing the precision, reliability, as well as productivity with respect to electronic devices. Researchers are actively contributing to the advancement of a digitized healthcare system by connecting various medical resources and healthcare services. Nevertheless, remote monitoring and management of elderly patients remain a formidable challenge for the latest technologies. In this research, an IoT-based healthcare system aimed at monitoring specialized IoT devices designed to track vital signs such as temperature, toileting habits, blood pressure, as well as sleep patterns. Furthermore, this system is equipped to automatically notify the relevant medical authorities of any potential risks faced by patients by continuously monitoring their real-time data and sending alerts via email. We believe that this study will prove valuable to both researchers and healthcare practitioners by offering insights into the significant potential of IoT in the medical domain while shedding light on the major challenges associated with IoT applications in healthcare. This work will also help the researchers to understand the applications of IoT in the healthcare domain. This contribution will offer an extensive exploration of IoT-based healthcare monitoring systems, offering a roadmap for the benefit of future researchers, scientists, and academicians by establishing a novel IoT-based healthcare monitoring system with the potential to revolutionize healthcare by leveraging modern technology to enhance patient care and overall quality of life.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".