Digital Twin Empowered Wireless Healthcare Monitoring for Smart Home
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
The dramatic progresses of wireless technologies and wearable devices have significantly promoted the development and popularity of smart home, while digital twin (DT) emerges as a game changer benefiting from its enhanced capabilities of visualization and interaction. The DT is able to build a realtime and continuous visual replica of a physical object or process, and to provide realtime monitoring, anomaly prediction, smart interaction, and lifecycle management. This paper presents a DT model to empower healthcare monitoring in the smart home with the goals of graphical monitoring, healthcare prediction, and intelligent control. High fidelity DT of the house and its equipments is created for visualized monitoring, and two suites of devices are deployed for continuously acquiring the users’ electrocardiograph (ECG) waves and the WiFi signals in the house. Two intelligent algorithms are then developed to perform fall detection from WiFi signals and to screen atrial fibrillation from ECG waves collected by wearable devices. Experimental results well validate the proposed model’s effectiveness for smart home monitoring, and the advantages of the developed smart algorithms for healthcare prediction over counterparts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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