International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 03 | Mar 2025
p-ISSN: 2395-0072
www.irjet.net
Cloud-Based IoT Coma Patient Monitoring System Utilizing Li-Fi Technology Dr Supriya Dinesh1, Ankush Sanga2, Aniruddha Gawande3, Atharva Bhondave4 1Professor, E&TC Engineering, JSPM’S Imperial College Of Engineering, Maharashtra, India
2,3,4Student, E&TC Engineering, JSPM’S Imperial College Of Engineering, Maharashtra, India
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Abstract - In intensive care environments, monitoring coma patients with precision and reliability is crucial. Utilizing Li-Fi technology and IoT for patient monitoring allows continuous tracking of vital signs such as heart rate, body temperature, oxygen saturation, and respiratory rate. By integrating Li-Fi with IoT, the approach enhances patient safety and ensures continuous, reliable monitoring, representing a significant advancement in healthcare technology. The system uses the Arduino Mega to collect vital data from sensors and the ESP8266 esp- 01 for fast wireless transfer. Data is streamed in real-time to ThingSpeak, a cloud platform for IoT analytics, for processing and analysis. The Prototypes effectiveness is validated across various lighting conditions. Integrating LiFi with IoT offers a robust solution for continuous monitoring of coma patients, significantly improving healthcare quality and safety.
broader frequency range (around 4 × 1014 to 7.5 × 1014 Hz) compared to Wi-Fi’s microwave range (2.4 GHz to 5 GHz), allowing for significantly higher data rates and enhanced coverage in well-lit areas.
Fig -1: Electromagnetic Spectrum
Key Words: Remote health monitoring, LiFi, IoT, Cloud telemedicine, healthcare support.
The proposed model uses Arduino Mega 2560 as the core con- troller to interface multiple biosensors, including MAX30100 for heart rate and SpO2, MPU6050 for motion detection, and DHT11 for temperature and humidity measurements. An ISD1820 module provides pre-recorded voice alerts, which are transmitted through LED light signals and received by a solar panel, establishing a sound-based LiFi communication link. Simultaneously, the ESP8266 - R1 Wi-Fi module uploads sensor data to the ThingSpeak cloud platform, allowing remote access to patient information. Developing an efficient monitoring system presents challenges such as ensuring continuous operation, reliable data transmission, and minimizing false alerts. The combination of Li-Fi and IoT creates a dual-alert mechanism, enhancing system reliability and response time. This hybrid approach aims to reduce healthcare staff workload while enabling faster and more accurate patient monitoring.
1. INTRODUCTION Healthcare systems require continuous monitoring of critical patients, especially coma patients, who are unable to communicate their physical conditions. Manual monitoring of such patients is time-consuming, prone to human errors, and can delay emergency response. Traditional patient monitoring systems often rely on Wi-Fi-based communication protocols, which are vulnerable to electromagnetic interference (EMI) in hospital environments, posing risks to both patient safety and sensitive medical equipment. This creates a need for a reliable and automated patient monitoring system that provides real- time data without interference. Li-Fi [1] technology, pioneered by Harald Haas in 2011, uses visible light for data transmission, emerging as a promising solution for healthcare systems. Unlike Wi-Fi [2], Li-Fi offers faster, secure, and interference-free communication, making it ideal for hospital environments. This project proposes a Cloud-Based IoT Coma Patient Monitoring System Utilizing Li-Fi Technology to continuously monitor patient vitals such as heart rate, oxygen saturation, temperature, and motion. The system integrates Li-Fi with IoT[3] to ensure seamless data transmission locally and remotely via cloud[4] platforms. Additionally, Li-Fi operates within the visible light spectrum (approximately 400 to 700 nm), which supports a
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This paper presents the design, working methodology, and theoretical analysis of the proposed system, highlighting its potential to enhance patient care in healthcare environments. By leveraging the high-speed, interferencefree nature of Li- Fi and the real-time data accessibility provided by IoT, the system ensures faster emergency responses and reduces the burden on healthcare professionals. Furthermore, the combination of cloud integration and dual-alert mechanisms ensures continuous monitoring, making it a reliable solution for critical patient care.
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