A Viewpoint-Independent Statistical Method for Fall Detection

Abstract

The goal of a fall detection system is to automatically detect cases where a human falls and may have been injured. We propose a statistical method based on Kinect depth cameras, that makes a decision based on information about how the human moved during the last few frames. Our method proposes novel features to be used for fall detection, and combines those features using a Bayesian framework. Our experiments explicitly evaluate the ability of our method to use training data collected from one viewpoint, in order to recognize falls from a different viewpoint. We obtain promising results, on a challenging dataset, that we have made public, and that contains, in addition to falls, several similarlooking events such as sitting down, picking up objects from under the bed, or tying shoelaces. 1.