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Updated: May 4, 2026

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

Secured framework for IoT based healthcare application.

Vidhya Nagavel¹, P T V Bhuvaneswari², Parameswaran Ramesh²

¹Department of Electronics Engineering, Madras Institute of Technology, Anna University, Chennai, Tamil Nadu, India. vin.vidhya612@gmail.com.

Scientific Reports

|May 2, 2026

Summary

This study introduces a secure Internet of Things (IoT) healthcare framework using machine learning and blockchain for enhanced data integrity and intelligent health risk assessment.

Keywords:

Blockchain Data security Diabetes prediction Internet of Things (IoT)Machine learning Smart Healthcare

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Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

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Published on: February 3, 2023

Area of Science:

Digital Health
Cybersecurity
Machine Learning

Background:

The Internet of Things (IoT) enables continuous health monitoring but faces significant cybersecurity challenges regarding data integrity and privacy.
Sensitive healthcare information requires robust protection against growing cyber threats in digital healthcare environments.

Purpose of the Study:

To propose a secure IoT-based healthcare framework integrating machine learning and blockchain for data protection.
To enhance the integrity and security of healthcare data and enable intelligent health risk assessment.

Main Methods:

The framework employs cryptographic hashing, Merkle trees, digital signatures, and a threshold-based blockchain validation mechanism.
Machine learning models were trained and evaluated on medical datasets for disease risk prediction.
The system was implemented in Python and deployed in a cloud environment for scalability.

Main Results:

The proposed blockchain validation mechanism effectively verified data integrity under simulated tampering, replay, and high-load scenarios.
Machine learning models achieved reliable predictive performance, measured by accuracy, precision, recall, and F1-score.
The framework demonstrated improved healthcare data integrity verification and robust predictive capabilities.

Conclusions:

The integrated machine learning and blockchain framework offers a secure and reliable solution for digital healthcare applications.
The study highlights the potential of combining IoT, blockchain, and machine learning for advanced health monitoring and risk assessment.