Convolutional neural networks (CNN) for indoor human activity recognition using Ubisense system

Abstract

In order to improve the accuracy of Indoor Human Activity Recognition based on the spatial location information, we proposed a recognition method using the convolutional neural network(CNN). We pre-process the raw spatial location data and transfer them into motion feature, frequency feature and statistic feature. These features are input into the CNN to do local feature analysis. After that, we got the characteristic output items, which have to be processed by the Softmax classifier, which can recognize six activities, including walking, sitting, lying, standing, jogging and jumping. By comparing the experimental results, the best recognition rate of different experimenters is 86.7%, which shows its feasibility.