Integrating Medical and Wearable Devices with E-Health Systems Using Horizontal IoT Platforms
Bibliographic record
Abstract
The Internet of Things (IoT) has unleashed the potential to expand the deployment of e-health systems by facilitating remote monitoring and introducing emerging IoT-enabled devices. However, this requires e-health systems to dynamically integrate a variety of heterogeneous IoT-enabled devices. In this paper, a four-phase device integration process is proposed to connect IoT-enabled medical devices with e-health systems through the use of horizontal IoT platforms. Horizontal platforms are used to facilitate the management of heterogeneous devices and their huge amount of data. The IoT-enabled medical devices are classified into direct and indirect devices based on their data availability to the platform. Data collection from direct devices is performed through wired or wireless communication. In contrast, indirect devices make their data available only to the cloud, from where the platform can access and retrieve the data. The proposed integration process is then implemented using OM2M horizontal IoT platform through two use cases of integrating direct and indirect real devices. For each use case, the technical details of the implementation phases are provided to demonstrate the effectiveness of the proposed device integration process. Finally, technical considerations and challenges are discussed for generalizing the proposed integration process to other medical devices.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".