  Falls are a major cause of injuries and deaths among older adults worldwide.
Accurate fall detection can help reduce potential injuries and additional
health complications. Different types of video modalities can be used in a home
setting to detect falls, including RGB, Infrared, and Thermal cameras. Anomaly
detection frameworks using autoencoders and their variants can be used for fall
detection due to the data imbalance that arises from the rarity and diversity
of falls. However, the use of reconstruction error in autoencoders can limit
the application of networks' structures that propagate information. In this
paper, we propose a new multi-objective loss function called Temporal Shift,
which aims to predict both future and reconstructed frames within a window of
sequential frames. The proposed loss function is evaluated on a
semi-naturalistic fall detection dataset containing multiple camera modalities.
The autoencoders were trained on normal activities of daily living (ADL)
performed by older adults and tested on ADLs and falls performed by young
adults. Temporal shift shows significant improvement to a baseline 3D
Convolutional autoencoder, an attention U-Net CAE, and a multi-modal neural
network. The greatest improvement was observed in an attention U-Net model
improving by 0.20 AUC ROC for a single camera when compared to reconstruction
alone. With significant improvement across different models, this approach has
the potential to be widely adopted and improve anomaly detection capabilities
in other settings besides fall detection.
