Classification of Human Activity by Using a Stacked Autoencoder

This paper investigates the application of a deep neural network architecture that consists of stackted autoencoder with two autoencoders and a softmax layer for the purpose of human activity classification. Th performance of the proposed architecture is tested on a commonly used data set known as Human Activity Recognition Using Smartphones. It is observed that the proposed method yields better classification results than the representative state-of-the-art methods provided that the parameters of the deep network are suitably optimized.