Fog and IoT-based Remote Patient Monitoring Architecture Using Speech Recognition
Bibliographic record
Abstract
Health care services have become a high demand due to the rise in medical technology. As a result, related resources are being depleted. Hospitals no longer have any space to accommodate for incoming patients. Remote Patient Monitoring (RPM) is a solution to this issue by creating a convenient and easy to access healthcare service. However, RPM systems are constrained by concerns on patient privacy, response time, and patient-service interaction. Patients emphasize their privacy, which requires health care services to maintain the confidentiality of their patient’s information. Wearable health monitors continuously transmit data. This feature results in high volumes of data transmissions towards the servers. In the current state of wearable devices, there is a lack of giving the patient an integrated means of interacting with the healthcare centre and vice versa. In this paper, we propose an architecture that uses fog computing and Internet of Things (IoT) devices to an already existing RPM system and addresses these challenges. The introduced system enables the health care providers to verify any of their data through a local server before it is reported to the main server. Also, this design incorporates a data filter that controls the outgoing data to maintain patient privacy. Finally, the inclusion of a local server offloads the extra data processing that is required from the server for a better flow of data. Tests in latency were executed to investigate the feasibility of a scalable fog architecture against a standard cloud-device setup. The results show that the proposed fog setup yielded significantly lower latencies under an increasing number of RPM rooms compared to the cloud setup. Results further support the fog and IoT-based architecture as a potential option for a scalable RPM.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".