Key result
Wearable IoT system with machine learning enables continuous vital sign monitoring and real-time alerts.
Why the study?
Affordable, scalable IoT-based remote monitoring systems are needed to improve healthcare access, support early diagnosis, and enhance patient safety through continuous real-time health tracking.
A proposed IoT-based wearable monitoring system successfully demonstrates the technical feasibility of using machine learning for real-time, continuous vital sign analysis and alert generation.
Prototype shows feasibility of IoT-ML wearables for real-time cardiac alerts; prospective validation required before clinical adoption.
This paper introduces an intelligent vital signs monitoring system that combines wearable IoT devices with machine learning to enable continuous, real-time health tracking. Sensors collect key data like heart rate, blood oxygen and send it to an ESP32 microcontroller, which processes and uploads the information to the cloud. Machine learning algorithms then analyze the data to classify normal vs abnormal conditions and predict potential health risks. An alert mechanism notifies patients or caregivers immediately if something unusual is detected. This affordable, scalable design is intended to improve healthcare access, support early diagnosis, and enhance patient safety[4]. Such IoT-based remote monitoring has been shown to improve quality of life by enabling secure, real-time patient observation[3].
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G. Himabindu, Darnasi Venkat, Guntamukkala Venkata Dinesh, Athina Venkata Ram Gopa, Avulamanda Subhash Babu (2026) studied Abnormal vital signs and heart defects. Wearable IoT intelligent vital signs monitoring system with machine learning was evaluated on Classification of normal vs abnormal health conditions and real-time alert generation. The proposed IoT-based wearable monitoring system integrating machine learning successfully collected continuous physiological data and classified health conditions to enable real-time alerts.
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