Project

Non-Invasive Real-Time Monitoring of Blood Pressure and Blood Glucose through Photoplethysmography leveraging IoMT and AI 

Cardiovascular diseases (CVDs) and diabetes are major health concerns globally, contributing significantly to mortality rates. Effective monitoring of these conditions is crucial for timely intervention and management. Traditional methods for monitoring blood pressure (BP) and blood glucose levels are often invasive, time-consuming, and costly, posing challenges, particularly in remote healthcare scenarios. 

In this project, we propose the development of non-invasive monitoring techniques using photoplethysmography (PPG) coupled with deep learning algorithms. PPG offers a promising avenue for continuous monitoring due to its ubiquity and ease of use. By leveraging deep learning techniques, we aim to create accurate and reliable models for estimating both blood pressure and blood glucose levels. 

The proposed solutions have the potential to revolutionize healthcare by enabling remote monitoring and timely intervention for individuals at risk of CVDs and diabetes. By providing accessible and non-invasive monitoring solutions, this project aims to improve the quality of healthcare delivery, particularly in resource-constrained settings. 

Publication Details 

• Padmavilochanan, Durga, Rahul Krishnan Pathinarupothi, KA Unnikrishna Menon, Harish Kumar, Ramesh Guntha, Maneesha V. Ramesh, and P. Venkat Rangan. “Personalized diabetes monitoring platform leveraging IoMT and AI for non-invasive estimation.” Smart Health 30 (2023): 100428. 
• A. Krishna M.P., P. Durga and R. K. Pathinarupothi, “Development of a Neural Network based model for Non-obtrusive Computation of BP from Photoplethysmograph,” 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh, 2020, pp. 1652-1655, doi: 10.1109/TENSYMP50017.2020.9230637. 

Patent Details 

• Ramesh, Maneesha Vinodini, Rahul Krishnan Pathinarupothi, Ekanath Srihari Rangan, P. Durga, and P. Venkat Rangan. “Systems, methods, and devices for remote health monitoring and management using  internet of things sensors.” U.S. Patent 10,433,726, issued October 8, 2019. 
• Ramesh, Maneesha Vinodini, Rahul Krishnan Pathinarupothi, and Ekanath Srihari Rangan. “Systems, methods, and devices for remote health monitoring and management.” U.S. Patent 10,542,889, issued January 28, 2020.