SmartWear: real-time Edge AI for personalized health monitoring in chronic disease management

The prevalence of chronic diseases necessitates continuous health monitoring and personalized management strategies to optimize patient outcomes. Traditional healthcare approaches, reliant on periodic assessments, often fail to provide the real-time insights needed for effective chronic disease management. This research introduces SmartWear, an innovative system integrating Edge AI with wearable health monitors to address this critical gap. SmartWear employs advanced, lightweight AI algorithms deployed directly on wearable devices, enabling real-time data analysis and personalized health recommendations. By processing data locally, SmartWear minimizes latency and enhances data privacy, thereby addressing the challenges of bandwidth consumption and sensitive information transmission. The primary objectives of this research are to develop efficient Edge AI models tailored for wearable devices, create adaptive health intervention systems, and ensure robust data security through on-device processing and federated learning. The focus is specifically on chronic conditions such as hypertension, diabetes, and chronic obstructive pulmonary disease (COPD), where continuous monitoring can significantly impact patient care. This study promises to advance the field of IoMT by offering a practical, scalable solution for real-time health monitoring and personalized care. The anticipated contributions include the introduction of novel AI techniques optimized for edge computing, the implementation of a user-friendly system that supports proactive health management, and the enhancement of patient outcomes through timely, personalized interventions. SmartWear represents a significant step forward in leveraging Edge AI to revolutionize chronic disease management and personalized healthcare.