Integrated Embedded IoT System for Dynamic Digital Twinning and Real-Time Bio-Behavioral Health Analytics
Year: 2025
Type: article
Abstract: This paper introduces an innovative embedded IoT system designed for real-time acquisition and cloud-based analysis of multi-dimensional bio-behavioral parameters. Integrating advanced sensors for physiological signals such as heart rate, temperature, finger movement, EEG, and ECG with robust wireless communication (ESP8266, GSM, GPS), the proposed platform provides comprehensive, continuous data to an AWS-powered cloud infrastructure. The system leverages this interoperable data for advanced analytics, facilitating digital twinning of user health profiles to enable personalized diagnostics, medication guidance, ergonomic product design, and behavioral insights. Uniquely, the platform incorporates mental and physical status recognition such as detecting sense of urination and emotional states through embedded intelligence, enabling new dimensions of preventive and participatory healthcare. This dynamic data pipeline supports not only enhanced clinical care, but also applications in insurance claim validation, product development, and remote patient management, enabled by resilient power backup and real-time alerting systems. The multidisciplinary design aims to bridge the gap between medical monitoring, digital twin technology, and user-focused analytics, thus establishing a new paradigm for integrated, actionable health management in both clinical and consumer contexts.
Institution: Rajiv Gandhi University of Knowledge Technologies
Language: English
Topic: Internet of Things and AI
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4415544132