Development of a Mobile Video Platform for Diagnosing Human Motor Anomalies

Abstract

The growing demand for telemedicine services necessitates innovative solutions for monitoring patients with movement disorders. The proposed mobile video surveillance system integrates computer vision, deep learning, and intelligent analysis methods to diagnose human movement anomalies accurately in real-time. The system's key components are algorithms for detecting and tracking human movements, contextual analysis, and a decision-making system. The system makes decisions about the detection or possibility of incidents, such as falls, and sends notifications to users via a mobile application. The integration of IoT sensors enables the processing of physiological data (heart rate, activity), which increases the accuracy of diagnostics and expands the system's range of applications. The proposed modular architecture of the system provides scalability and adaptability to different operating conditions. The system meets privacy standards through the use of encryption and multi-level authentication. Our proposed system ensures effective monitoring of patients with motor disorders, detects incidents promptly, and sends alerts, improving the quality of telemedicine services.