| Parkinson's disease (PD) detection and monitoring through the development of an integrated system leveraging Internet of Things (IoT) technology and cloud computing. Parkinson's disease, a progressive neurodegenerative disorder, poses significant challenges in early diagnosis and personalized treatment. By integrating physiological sensors, such as ECG sensors and gyroscopes, with a Node MCU microcontroller and a cloud environment, this system aims to capture, analyze, and interpret relevant physiological data indicative of PD symptoms. The study begins with a comprehensive review of the literature, highlighting the existing approaches to PD detection and monitoring, as well as the limitations of current systems. Building upon this foundation, the proposed integrated system architecture is outlined, detailing the roles of each component in data collection, transmission, storage, and analysis. Physiological sensors, including ECG sensors for heart rate variability and gyroscopes for movement patterns, serve as the primary data sources, capturing real-time data from patients. The Node MCU microcontroller acts as the central processing unit, facilitating data preprocessing and transmission to the cloud environment. IoT communication protocols enable secure and efficient data transmission between the Node MCU and the cloud, where advanced analytics techniques, such as machine learning algorithms, are applied to identify patterns indicative of PD progression. The cloud environment provides the infrastructure and resources necessary for data storage, processing, and analysis, ensuring scalability, reliability, and security. Preliminary results from a pilot study demonstrate the feasibility and effectiveness of the integrated system in capturing physiological data and detecting PD symptoms. By providing real-time insights into patients' health status and facilitating remote monitoring and assessment, this system has the potential to revolutionize PD management, enabling early intervention and personalized treatment strategies. However, further research and validation are needed to optimize the system's performance and scalability for widespread clinical use. Overall, this study presents a promising approach to PD dete |
