DeepFall -- Non-invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders
This addresses health and safety concerns by providing a non-invasive method for detecting rare falls, though it is incremental as it builds on existing autoencoder techniques.
The paper tackles fall detection by framing it as an anomaly detection problem using deep spatio-temporal convolutional autoencoders, achieving superior results compared to traditional autoencoder methods on three public datasets.
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.