  Fall detection holds immense importance in the field of healthcare, where
timely detection allows for instant medical assistance. In this context, we
propose a 3D ConvNet architecture which consists of 3D Inception modules for
fall detection. The proposed architecture is a custom version of Inflated 3D
(I3D) architecture, that takes compressed measurements of video sequence as
spatio-temporal input, obtained from compressive sensing framework, rather than
video sequence as input, as in the case of I3D convolutional neural network.
This is adopted since privacy raises a huge concern for patients being
monitored through these RGB cameras. The proposed framework for fall detection
is flexible enough with respect to a wide variety of measurement matrices. Ten
action classes randomly selected from Kinetics-400 with no fall examples, are
employed to train our 3D ConvNet post compressive sensing with different types
of sensing matrices on the original video clips. Our results show that 3D
ConvNet performance remains unchanged with different sensing matrices. Also,
the performance obtained with Kinetics pre-trained 3D ConvNet on compressively
sensed fall videos from benchmark datasets is better than the state-of-the-art
techniques.
