Paper Title
Decreasing Parameters of Deep Neural Network Model for Fall Detection

The goal of this work is to achieve a small model which have huge possibilities to implements on small embedded system. We use convolutional neural network (CNN) to classify human’s 3D skeleton information as falling cases. After deploying several simple rules, the network is small enough to execute without CUDA support platform and retains the accuracy over 99% on NTURGB+D dataset. Keywords - Deep Neural Network, Fall Detection, Public Health Problem, Artifactual Intelligence