Low complexity human fall detection using body location and posture geometry

Abstract

This paper presents the human fall detection using body location (HFBL) and posture geometry. The main contribution of the proposed HFBL system is to reduce the computational complexity of fall detection system while maintaining accuracy, as most fall detection techniques rely on computationally complex algorithms from machine learning or deep learning. This approach examines the human posture by applying the image segmentation and ratio by posture geometry. Then, the distance transform is used to calculate the high brightness points on the human body. These points are the maximum values compared with the edge values. Afterward, one of these points is selected as a center point. A line is formed by this center point aligned horizontally to separate the upper area and lower area, then an intersection line is drawn through this center point vertically that can separate the four quadrants of body location. With the help of posture geometry, the angles are employed for prediction “Fall” or “NotFall” actions at each frame of video sequence. Referring to the dynamic balance, the ratio between the distance vectors from the center point to the right and left legs is calculated to confirm fall and non-fall activities, utilizing the Pythagorean trigonometric identity. For experiments, 2,542 images from the UR fall detection dataset, with dimensions of 640×480×3 were prepared through image segmentation to find the human body shape for analysis using the proposed HFBL system. Results demonstrate that the low computational HFBL approach can provide 91.23% accuracy, the precision value is 99.14%, the recall value is 84.48%, and the F1-score value is 91.22%.