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TRUVACE RECORD VERSION
record: TRV-2026-0143
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-13T08:51:24.973263Z
status: published
lens: g_space
sector: health
headline: ZigBee Based Low Latency IoT and AI Integrated Framework for Real Time Telehealth Monitoring
dek: The Internet of Things (IoT) and Artificial Intelligence (AI) have opened up new frontiers in remote health monitoring with the integration of technologies and transformative solutions in order to detect real-time health monitoring and disparities. This article shows an innovative and integrated wireless health surveillance system, which is aimed at auxiliary environments, especially for elderly and chronically ill patients. The system links IoT sensors to monitor heart rate, body temperature, and oxygen level w…
gain_title: In a real-world care facility test by April 2026, the ZigBee-based AI telehealth framework monitoring elderly and chronically ill patients achieved 95% accuracy with 100% recall for health discrepancy detection while operating at 120 ms transmission delay and 3.8 mW/h power consumption.
problem_title: (none)
trace_subject: (none)
gain_reading: In a real-world care facility test by April 2026, the ZigBee-based AI telehealth framework monitoring elderly and chronically ill patients achieved 95% accuracy with 100% recall for health discrepancy detection while operating at 120 ms transmission delay and 3.8 mW/h power consumption.
gain_evidence: random forest model in particular gets an impressive 95% accuracy and recalls 100% | just a 120 ms delay and a power consumption of 3.8 mW/h
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers built and tested a ZigBee-based wireless system that connects wearable sensors for heart rate, temperature and oxygen to cloud AI models including random forest, SVM and logistic regression. By April 2026, tests in a care facility reported 95% accuracy, 100% recall, 120 ms delay and 3.8 mW/h power use.

The result matters because low-latency, low-power continuous monitoring could reduce false negatives and clinician burden for elderly and chronically ill patients, but the source does not report sample size, duration, comparator performance, or clinical outcomes such as hospitalizations avoided.
limitation: 
tag: Evidence-backed gain
key_points: System links IoT sensors for heart rate, body temperature, and oxygen level to cloud-based AI-driven systems for continuous real-time monitoring. | Uses ZigBee protocol for low-power, reliable communication from wearer to centralized processing unit. | Tested machine learning models include random forest, support vector machine (SVM), and logistic regression. | Modular structure allows addition of blood pressure and glucose monitors for scalability.
rundown: The framework was described as an innovative and integrated wireless health surveillance system aimed at auxiliary environments, especially for elderly and chronically ill patients, linking wearable IoT sensors to a centralized processing unit via ZigBee.

Testing in a real-world care facility measured system performance at 120 ms delay and 3.8 mW/h power consumption, positioned as suitable for long-term deployment, with a modular design intended to add blood pressure and glucose monitors.
sources:
- peer_reviewed | Emerging Science Journal | https://doi.org/10.28991/esj-2026-010-02-024 | 2026-04-01
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