Analysis of Disease Diagnosis and Monitoring Methods Based on Wearable Devices
DOI:
https://doi.org/10.54097/3yrqv956Keywords:
Wearable Devices, Sensor Technology, Heart Disease, Parkinson'Disease, Diabetes.Abstract
With the rapid advancement of sensor technology, artificial intelligence, and the Internet of Things (IoT), wearable devices have demonstrated tremendous potential in healthcare, providing revolutionary tools for early disease diagnosis, continuous monitoring, and personalized management. This review focuses on wearable technology applications in four major chronic diseases: heart disease, Parkinson's disease, diabetes, and depression. The article first outlines key physiological and behavioral parameters collected by wearables, such as heart rate variability, gait patterns, blood glucose levels, and sleep quality, then systematically analyzes core diagnostic and monitoring methodologies for these conditions. It summarizes critical biomarker identification strategies, mainstream data analysis algorithms, clinical validation outcomes, and practical advancements in disease prediction, condition assessment, and rehabilitation management. Future wearable technologies hold significant potential to deliver continuous, objective, real-time, and accurate health data, while also addressing challenges related to data privacy, user compliance, and regulatory approval. Through multi-modal data integration and the development of advanced AI algorithms, chronic diseases can achieve enhanced treatment and management, ultimately improving patient outcomes and quality of life.
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