Real-time Medical Emergency Response System: Exploiting IoT and Big Data for Public Health.

Area of Science:

• Health Informatics
• Internet of Things (IoT)
• Big Data Analytics

Background:

• Nations require healthy populations for development.
• Continuous health monitoring via Internet of Things (IoT)-based Body Area Networks (BANs) is growing for real-time emergency response.
• Massive heterogeneous data from millions of body-attached sensors (Big Data) pose processing challenges.

Purpose of the Study:

• To propose a Real-time Medical Emergency Response System.
• To address the challenge of processing Big Data from continuous health monitoring for critical situations.
• To enable real-time actions during medical emergencies using IoT-based medical sensors.

Main Methods:

• Development of a layered architecture and implementation model for an "Intelligent Building" data analysis center.
• Utilizing IoT-based medical sensors deployed on the human body for data collection.
• Implementing the system on Hadoop for processing and analysis, including collection, Hadoop Processing Unit (HPU), and decision-making units.

Main Results:

• The proposed system demonstrates efficient processing of Wireless Body Area Network (WBAN) sensory data from millions of users.
• Feasibility and efficiency were validated using various medical sensory datasets and real-time network traffic.
• The system is capable of performing real-time responses crucial for medical emergencies.

Conclusions:

• The developed Real-time Medical Emergency Response System effectively handles Big Data from WBANs.
• The "Intelligent Building" approach facilitates efficient analysis and decision-making for large-scale health monitoring.
• The system provides a viable solution for real-time medical emergency response in a national context.