  Fall detection and classification become an imper- ative problem for
healthcare applications particularity with the increasingly ageing population.
Currently, most of the fall clas- sification algorithms provide binary fall or
no-fall classification. For better healthcare, it is thus not enough to do
binary fall classification but to extend it to multiple fall events
classification. In this work, we utilize the privacy mitigating human skeleton
data for multiple fall events classification. The skeleton features are
extracted from the original RGB images to not only mitigate the personal
privacy, but also to reduce the impact of the dynamic illuminations. The
proposed fall events classification method is divided into two stages. In the
first stage, the model is trained to achieve the binary classification to
filter out the no-fall events. Then, in the second stage, the deep neural
network (DNN) model is trained to further classify the five types of fall
events. In order to confirm the efficiency of the proposed method, the
experiments on the UP-Fall dataset outperform the state-of-the-art.
