Recognizing a Mobile Phone's Storing Position as a Context of a Device and a User

A mobile phone is getting smarter by employing a sensor and awareness of various contexts about a user and the terminal itself. In this paper, we deal with 9 storing positions of a smartphone on the body as a context of a device itself and a user: 1) around the neck (hanging), 2) chest pocket, 3) jacket pocket (side), 4) front pocket of trousers, 5) back pocket of trousers, 6) backpack, 7) handbag, 8) messenger bag, and 9) shoulder bag. We propose a method of recognizing the 9 positions by machine learning algorithms with 60 features that characterize specific movements of a terminal at the position during walking. The result of o✏ine experiment showed that an overall accuracy was 74.6% in a strict condition of Leave-One-Subject-Out (LOSO) test, where a support vector machine (SVM) classifier was trained with dataset from other subjects.