Best feature for CNN classification of Human Activity using IOT network

In this paper, we design an experience that evaluates what is the best descriptor to recognize human activity using Convolutional Neural Network (CNN) in a non-controlled environment using a network of smart objects. We chose to classify four types of activities: standing, sitting, laying and walking. We selected a set of the most popular and suitable descriptors and do a comparative study of the classification results using different classifiers. Results show that the discrete cosine transform (DCT), with the convolutional neural network (CNN) as a classifier, achieves more than 98% average accuracy by choosing a certain provision of the network of smart objects on the body. Therefore, the selection of descriptors saves computation time and memory space, reaching high classification accuracy.