Enhanced Fall Detection in Elderly Care Using Motion History Image and Correlation Factor

Falls are a significant concern within the senior care system, often leading to serious injuries and health complications. This article introduces a novel approach for detecting falls using Motion History Image (MHI) and a correlation factor to enhance the accuracy and responsiveness of fall detection systems. The proposed method is evaluated using key performance metrics, including sensitivity, specificity, precision, and classification accuracy, providing a comprehensive assessment of its effectiveness. The UR dataset is employed to test the method, and results demonstrate that the approach delivers superior sensitivity compared to contemporary techniques. These findings suggest that the proposed method is a reliable solution for improving fall detection within the senior care system.