An AI-Driven Wearable Framework for Remote Health Monitoring and AR-Based Notifications
Bibliographic record
Abstract
The rising rate of chronic diseases and the aging population of the world is a problem that highlights the need to have real-time health monitoring solutions that are proactive. The current wearable gadgets do not offer much predictive data and have restricted clinical functionality as they offer generic, reactive notifications with only partial predictive power. In this paper, a healthcare framework, called ARHealthMonitor, which relies on AI and uses smartwatches and AR-enabled smart glasses as its foundational elements to provide continuous and individualized health monitoring and automated emergency response is discussed. Multimodal sensors are used to record cardiovascular and activity measurements and a cloud-edge AI processor is used to set individual baselines, detect anomalies, and minimize false alarms by 35% over threshold-based models. Trial prototype testing of 50 subjects showed fall detection sensitivity of 92 percent, less than 3 percent error in heart rate measurements and mean-time-to-alert (MTTA) of less than 15 seconds. The system combines healthcare processes with EHR/telehealth interoperability. The platform uses end-to-end encryption to align with the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR), and India's Digital Personal Data Protection (DPDP) Act, an independent audit will be sought in future work. Findings suggest that ARHealthMonitor can be used to turn consumer wearables into clinical-quality preventive care systems which can better the results of early intervention and prevent unnecessary emergencies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".