Reliable Internet of Things for Health Care Technologies
Bibliographic record
Abstract
Background Several at-home monitoring devices are being introduced in the market, which can help individuals, fitness enthusiasts, etc, monitor their health anytime they want. This allows individuals to monitor and collect their health data, reflect upon it, and take necessary action. Such technologies can help enhance the user’s quality of life by motivating and empowering them to improve their health actively. Unfortunately, there are still several challenges to making this transition from in-hospital monitoring to home monitoring smoother. Some of these challenges may include technology readiness and acceptance by patients and their family members, lack of proper privacy measures, security, and lack of reliable internet and communication technology infrastructure. Objective The objective of this study is to use wireless communication networks to remotely transfer data from various body sensors measuring different vital parameters. Wireless sensors (electrocardiogram monitors, sleep sensors, etc) and Internet of Things devices can allow real-time and relatively cheap at-home health monitoring to provide critical health updates over the internet. Methods The study will be conducted by means of designing multiple experiments in which data from different sensors will be collected, packaged, and sent to a remote server using the internet. Along with the patient data, different network performance parameters such as delay, information loss, etc, will be calculated to understand and evaluate network performance. Results The results from the experiment will focus on evaluating network performance parameters such as latency, delay, packet drop, etc, in various indoor as well as outdoor environments. Conclusions We hope the results obtained from these experiment can be used for making various technological design choices and serve as a good starting point while building Internet of Things health care technologies. Conflicts of Interest None declared.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".