Feature selection to detect fallen pose using depth images

In this paper we are interesting in knowing which features provide useful information for detecting a fall and how the set of selected characteristics impact the performance of detection. Then we define a large set of possible features, which are extracted from a cloud of points of a person by the kinect device, some of features were used in previous work, and we propose to add and evaluate the effect of using 3D moment invariants translation, scale an rotation, and other geometric characteristics. Two experiments are carried out to analyze the effect of using two different subset of features, one of them selected by a Genetic Algorithm and the second by Principal Component Analysis (PCA). The obtained results suggest that the success of detection of fall depends on the selected features, and the genetic algorithm is a good technique to select them, when compared with PCA.