Behavior analysis method for indoor environment based on app usage mining

Abstract

With the recent development of information and communication technologies, the utilization areas of spatial information are increasing rapidly while merging with various industries and technologies. This study introduces a system that can analyze the space utilization of users at low cost in indoor environment using simple smartphone app logs. We collect and process important information from mobile app logs and Google app server and generate a high-dimensional dataset required to analyze user behaviors. In addition, user behaviors are classified and clustered by applying a VGG classifier and a clustering algorithm based on t-stochastic neighbor embedding (t-SNE). Our system can easily acquire a large amount of data required for deep learning network learning without additional sensors for spatial analysis and enhance the accuracy of network classification and cluster through these data. Our methodology can assist spatial analysis for indoor environments in which people are living and help reduce the cost of space utilization feedback from users.