Internet-of-Things Devices and Assistive Technologies for Health Care: Applications, Challenges, and Opportunities
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
Medical conditions and cases are growing at a rapid pace, and physical space is starting to be constrained. Hospitals and clinics no longer have the ability to accommodate large numbers of incoming patients. It is clear that the health industry needs to improve the current state of its valuable and limited resources. The evolution of Internet-of-Things (IoT) devices along with assistive technologies can alleviate the problem in health care by providing a convenient and easy means of accessing health-care services wirelessly. There is a plethora of IoT devices and potential applications that can take advantage of the unique characteristics that these technologies can offer. However, at the same time, these services pose novel challenges that need to be properly addressed. In this article, we review some popular categories of IoT-based applications for health care along with their devices. We then describe the challenges and discuss how research can properly address the open issues and improve the already-existing implementations in health care. Further possible solutions, including machine learning (ML) techniques, are also discussed to show their potential as viable solutions for future health-care applications.
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 | 0.000 | 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) | 0.000 | 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