A Secure Li-Fi Modern Healthcare System Using Body Sensor Network and Machine Learning

A secure Li-Fi-based modern healthcare system using a body sensor network is designed for real-time patient monitoring with high-speed and secure data transmission. The system is built around an Arduino UNO as the main controller, interfacing with multiple sensors, including a heartbeat sensor to measure heart rate, a MEMS sensor to detect movement, an eyeblink sensor, and a Dallas temperature sensor for body temperature monitoring. A GSM module is integrated to send alerts when abnormal conditions are detected, while an LCD provides real-time feedback. The system consists of two modules, a transmitter (TX) kit and a receiver (RX) kit, where Li-Fi technology ensures secure communication between them. Additionally, a machine learning model using the Random Forest algorithm in Python is implemented to analyze sensor data and detect anomalies. If any abnormal activity is identified, an automatic alert message is sent to caregivers or healthcare providers. This system enhances real-time patient monitoring, improves emergency response time, and ensures a reliable and efficient healthcare communication framework