Iot Based Healthcare System for Paralysis Patient Using Machine Learning Algorithm

Healthcare project for paralysis patient presents a holistic assistive technology framework catering to the communication and health monitoring needs of individuals affected by paralysis. The system consists of two main components: the Body Tilt Communication System and Smart Support Gloves, both leveraging Arduino based technologies. The Body Tilt Communication System utilizes an Arduino Uno, ADXL Accelerometer, and LCD screen to enable message display through subtle body movements, while the Smart Support Gloves, incorporating Flex Sensors and Arduino Nano, facilitate straightforward communication for those with hearing impairments. In addition to communication enhancements, the system integrates health monitoring capabilities through Pulse, SPO2, and LM35 Temperature sensors. These sensors, connected to a NodeMCU, enable real time health data transmission to a central server. Machine learning algorithm (SVC) analyze this data, predicting potential health issues. In emergencies, alerts are automatically sent to registered caretakers via IoT, ensuring prompt response and care for paralyzed individuals.This amalgamation of Arduino based technologies, IoT connectivity, and machine learning provides a versatile and comprehensive solution, addressing both communication barriers and health concerns for paralysis patients. The project strives to significantly improve the quality of life for individuals facing the complexities of paralysis by promoting efficient communication and proactive health management.