Outlier Behavior Detection for Indoor Environment Based on t-SNE Clustering

In this study, we propose a low-cost system that can detect the space outlier utilization of residents in an indoor environment. We focus on the users’ app usage to analyze unusual behavior, especially in indoor spaces. This is reflected in the behavioral analysis in that the frequency of using smartphones in personal spaces has recently increased. Our system facilitates autonomous data collection from mobile app logs and Google app servers and generates a high-dimensional dataset that can detect outlier behaviors. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was applied for effective singular movement analysis. To analyze high-level mobile phone usage, the t-distributed stochastic neighbor embedding (t-SNE) algorithm was employed. These two clustering algorithms can effectively detect outlier behaviors in terms of movement and app usage in indoor spaces. The experimental results showed that our system enables effective spatial behavioral analysis at a low cost when applied to logs collected in actual living spaces. Moreover, large volumes of data required for outlier detection can be easily acquired. The system can automatically detect the unusual behavior of a user in an indoor space. In particular, this study aims to reflect the recent trend of the increasing use of smartphones in indoor spaces to the behavioral analysis.