Caroline Zhou1 and Ivan Revilla2, 1USA, 2California State Polytechnic University, USA
Parkinson's disease (PD) is a progressive neurological disorder that necessitates continuous and accurate monitoring for effective management [4]. We propose an innovative system that leverages video analysis and machine learning to predict clinical scores for PD patients. Our system includes a mobile application for recording and uploading videos, a cloud-based server for processing the data, and a machine learning model for analyzing the videos [5]. Key technologies employed include Flutter for the mobile app, Firebase for data storage and authentication, and advanced machine learning models such as Bayesian Ridge and Random Forest regression [6]. Challenges such as variability in video quality and limited dataset diversity were addressed through robust preprocessing techniques and plans to expand the dataset to include more diverse participants. Our experiments demonstrated that Bayesian Ridge and Random Forest regression models achieved high prediction accuracy for clinical scores. The results highlight the system's potential for providing a reliable and user-friendly method for monitoring PD. This comprehensive approach promises significant improvements in patient care and disease management, making it a valuable tool for both patients and healthcare providers.
Parkinson's Disease, Video Analysis, Machine Learning, Mobile Application
