Using Convolutional Layer Features for Indoor Human Activity Recognition based on Spatial Location Information

Abstract

Identifying human actions has great importance for various applications, especially in the smart home, fitness tracking and health monitoring domains. However, human activity recognition still remains a challenging task. This is mainly due to the broad range of human activities as well as the rich variation of a given activity can be performed. In this paper, we dealt with the problem by making use of spatial location information of three different parts of a human body, which are derived via three UWB (ultrawide band) tags and an Ubisense positioning system. In order to improve the accuracy, we proposed a recognition method: convolutional layer features plus SVM (Support Vector Machine). 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 (Convolutional Neural Network) to generate the convolutional layer features, and then we use SVM to classify these features. By comparing the experimental results, the best recognition rate of different experimenters is 89.75%, which shows its feasibility.

Periodic Pattern Detection of Printed Fabric Based on Deep Learning Algorithm

“…The methods in natural scenes cannot be used directly due to the abundant texture and shape features. Li J. et al [15] found that CNNs can implicitly learn spatial location information. The CNN learns the characteristic information of the primitives, and uses this characteristic information to segment the periodic pattern primitives of the printed fabric.…”