Falling Detection System based on Machine Learning

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 K-nearest neighbor (KNN) classification algorithms via achieving falling detection with accuracy of 96.59 %.