This project aims to design a complete remote patient monitoring system using AI and IoT-based technologies that measure patient’s vital signs, symptoms, and health metrics in real-time. Wearable sensors, medical devices, and mobile applications will supply data into the system for the processing of health abnormalities manifestation, follow-up of disease progress, and raising of alerts on early possible interventions. This would mean earlier diagnosis and thus treatment and enrollment at a later stage of illness for those with chronic diseases. The aim of this project is to foster timely and accurate monitoring, hence better patient outcomes, by enabling proactive healthcare management. The members will be data scientists, healthcare professionals, IoT engineers, and software developers.
Week 1-2: Initial Planning and Requirement Analysis
Define project objectives, scope, and high-level requirements.
Gather necessary data and resources.
Week 3-5: Data Collection and Preprocessing Phase
Collection of raw data from wearable sensors, medical devices, and mobile apps.
Preprocess data from wearable sensor, medical device, and mobile app-based data.
Extract data to identify which features could be pertinent for the monitoring system.
Week 6-8: Model Development and Training Phase
Develop and train machine learning models to detect health abnormalities and track disease progression.
Validate model accuracy and performance against historical data.
Week 9-10: System Integration and Testing Phase
Implementing the remote patient monitoring system.
Integration with existing healthcare management platforms.
Test and improve the system based on the performance metrics provided by the tracker and reception of user feedback.
Week 11-12: Deployment and Monitoring
System deployment to Healthcare Environment
Flow chart deploys the system into healthcare facilities and tracks its performance for necessary tuning
