A viewpoint-independent statistical method for fall detection

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 similar-looking events such as sitting down, picking up objects from under the bed, or tying shoelaces.