Low Cost Sensors Integrated Disease Diagnose and Remote Consultation System

¹ Department of Biomedical Engineering,Nandha Engineering College, Erode, India.
² Department of Artificial Intelligence and Data Science, Nandha Engineering College, Erode, India.

^* Thivyababha K: This email address is being protected from spambots. You need JavaScript enabled to view it.
Lalitha Krishnamoorthy: This email address is being protected from spambots. You need JavaScript enabled to view it.

Published online: 29 May 2026

Abstract

Reliable and affordable healthcare facility accessing is a major problem in rural areas and underdeveloped areas, like remote villages and tribal regions, where medical facilities like PHC and hospitals are often limited. To focus on these issues, this project work proposes a low-cost system for health monitoring and remote consultations that is intended to provide continuous and real-time monitoring of vital signs. The proposed model uses a group of biosensors, including LM35 for temperature, MQ135 for air quality, BMP280 for pressure, HRM2511E for heart rate, and a glucose sensor. These sensors measure vital health signs such as body temperature, air quality, and blood pressure, heart rate, and blood glucose levels. All sensors are interconnected to a microcontroller (Auduino) that gathers and sends data using LoRa (Long Range) communication protocol and the receiver at the healthcare provider end will receive the signals. Use of LoRa reduces the need for internet access and ensure dependable performance in remote areas or area with weak network connectivity. While experimenting that data transmission was proper up to limited range, with real-time monitoring of parameters on LCD screens and IoT-based dashboards. Additionally, SMS alerts were successfully sent for abnormal values. This shows the system's ability to support early detection and timely medical intervention. Unlike existing IoT healthcare systems that depends on continuous internet access, the proposed system will provide a low-power, offline, and affordable option for community health monitoring. Overall, this system provides a model for early disease detection, prevention, and teleconsultations, helps to reduce the gap in healthcare facilities for rural populations.