  Detecting unintended falls is essential for ambient intelligence and
healthcare of elderly people living alone. In recent years, deep convolutional
nets are widely used in human action analysis, based on which a number of fall
detection methods have been proposed. Despite their highly effective
performances, the behaviors of how the convolutional nets recognize falls are
still not clear. In this paper, instead of proposing a novel approach, we
perform a systematical empirical study, attempting to investigate the
underlying fall recognition process. We propose four tasks to investigate,
which involve five types of input modalities, seven net instances and different
training samples. The obtained quantitative and qualitative results reveal the
patterns that the nets tend to learn, and several factors that can heavily
influence the performances on fall recognition. We expect that our conclusions
are favorable to proposing better deep learning solutions to fall detection
systems.
