Design of Mixed Reality (MR) Based Real Time Vision System for TB Disease Tracking and Control

DOI

https://www.doi.org/10.59256/ijire.20250603007

Published In

Volume 6, Issue 3 (2025)

Published Online

May-June 2025

Pages

68-72

Article Type

Original Article

Check for updates

In today’s world, early detection and real-time monitoring of infectious diseases like tuberculosis (TB) are essential to safeguard public health. Traditional TB screening methods often rely on manual diagnosis and are limited in scalability and speed. The "Mixed Reality (MR) Vision System for TB Tracking" introduces a smart, contactless, and automated approach by integrating IoT-enabled finger-placed biomedical sensors with Mixed Reality (MR) technology and cloud computing. When a user taps the sensor, vital health parameters—such as SpO₂ level, body temperature, and heart rate—are captured and immediately displayed on an LCD screen, providing instant feedback to the individual. Simultaneously, the readings are transmitted to the Thing Speak cloud platform for secure and centralized storage. Each individual is assigned a unique MR code, which is automatically sent to their Telegram account and linked to their real-time health data. At public access points, MR vision cameras scan these MR codes and retrieve the individual's latest cloud-stored values. The system compares these with predefined thresholds stored locally, and provides a color-coded health status—green indicating safe and red flagging potential TB symptoms— enabling fast and automated health screening. This system offers a robust and scalable solution for real-time TB tracking in public spaces like airports, schools, and hospitals. A web interface allows healthcare professionals to manage and analyze patient data. By integrating IoT, MR, cloud computing, and user- interactive displays, this project enhances disease surveillance, reduces manual workload, and ensures timely intervention. Future improvements may include AI-driven symptom analysis, integration with national health databases, and additional sensor modules for expanded diagnostics.