  Human falls rarely occur; however, detecting falls is very important from the
health and safety perspective. Due to the rarity of falls, it is difficult to
employ supervised classification techniques to detect them. Moreover, in these
highly skewed situations it is also difficult to extract domain specific
features to identify falls. In this paper, we present a novel framework,
\textit{DeepFall}, which formulates the fall detection problem as an anomaly
detection problem. The \textit{DeepFall} framework presents the novel use of
deep spatio-temporal convolutional autoencoders to learn spatial and temporal
features from normal activities using non-invasive sensing modalities. We also
present a new anomaly scoring method that combines the reconstruction score of
frames across a temporal window to detect unseen falls. We tested the
\textit{DeepFall} framework on three publicly available datasets collected
through non-invasive sensing modalities, thermal camera and depth cameras and
show superior results in comparison to traditional autoencoder methods to
identify unseen falls.
