Learning-Aided User Identification Using Smartphone Sensors for Smart Homes

Smart homes expects to improve the convenience, comfort, and energy efficiency of the residents by connecting and controlling various appliances. As the personal information and computing hub for smart homes, smartphones allow people to monitor and control their homes anytime and anywhere. Therefore, the security and privacy of smartphones and the stored data are crucial in smart homes. To protect smartphones from potential attacks, various built-in sensors can be utilized for user authentication/identification and access control to achieve enhanced security. In this paper, we propose a framework, smartphone sensor user identification (SSUI), in order to facilitate user identification based on the relationships between different types of sensor data and smartphone users. Specifically in SSUI, the time and frequency features are extracted and learned separately using convolution neural network (CNN). The CNN outputs are then processed using recurrent neural network, according to several time bins. Using both of our own dataset (collected from 17 participants) and a publicly available dataset (i.e., Heterogeneity Dataset for Human Activity Recognition), we demonstrate the effectiveness of the proposed SSUI framework, where we achieve an accuracy rate of over 91.45% in various scenarios.