Falling Detection System Based on Machine Learning
Mai Nadi, Nashwa El-Bendary, Aboul Ella Hassanien, Tai-hoon Kim · 2015
As falling is the most important issue that faces elderly people all over the world, this paper proposes a detection system for falling based on Machine Learning (ML). In the proposed system, a dataset of videos containing falling actions has been utilized via dividing each video into many shots that are consequently being converted into gray-level images. Then, for detecting the moving objects in videos, the foreground is firstly detected, then noise and shadow are deleted to detect the moving object. Finally, a number of features, including aspect ratio and falling angle, are extracted and a number of classifiers are being applied in order to detect the occurrence of falling. Experimental results, using 10-fold cross validation, shown that the proposed falling detection approach based on Linear Discriminant Analysis (LDA) classification algorithm has outperformed both support vector machines (SVMs) and Knearest neighbor (KNN) classification algorithms via achieving falling detection with accuracy of 96.59 %.