Flexible Sensors for IoT-Based Health Monitoring
Bibliographic record
Abstract
The global population is aging due to increased life expectancy and declining birth rates. As a result, there is a growing prevalence of chronic diseases such as heart disease, hypertension, and diabetes, among the older population. These conditions not only diminish the quality of life but also significantly drive up healthcare costs. Consequently, the demand for efficient and cost-effective healthcare solutions is rising. Traditional healthcare systems are often challenged by issues of accessibility and equity, particularly in regions with inadequate medical infrastructure and geographic barriers. In response to these challenges, this article explores the potential of advanced flexible sensor technologies, integrated with cutting-edge communication and computing tools such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics. These sensors enable continuous, unobtrusive monitoring of vital signs, and health parameters, facilitating personalized and preventive care in the comfort of an individual’s home. However, the widespread adoption of these technologies faces several obstacles, including challenges related to manufacturing scalability, cost, mechanical stability, and data security. This article reviews the current state of research and development in flexible sensors and their integration with modern technologies for IoT-based health monitoring. It also examines key challenges and concerns associated with their use and outlines the future potential for these sensors to revolutionize healthcare monitoring and management.
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.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".