Inactivity Recognition: Separating Moving Phones from Stationary Users

Accurate methods of detecting whether a person is at rest form an important component in indoor localization and sedentary lifestyle monitoring. The problem of quantifying rest is complicated by the variety of activities and phone configurations that exist even when the user location is stationary. Our study examines whether on-phone kinematic sensors can be used to accurately and consistently detect rest. Rest is defined as a user's absolute positioning with respect to a world coordinate frame not changing significantly over a fixed time interval. An important requirement in our approach is that the algorithm maintains its accuracy independent of orientation and on-body location. The techniques examined show high accuracy classification (>95%) with test participants simulating typical everyday tasks in an office environment. An important contribution of our approach is showing that rest detection accuracy improved when accounting for the orientation of the phone for the activities discussed.