TRAcME: Temporal Activity Recognition Using Mobile Phone Data

The aim of human activity recognition is to identify what a user or a group of users are doing at a given point in time, for example travelling or working. Activity recognition plays an important role in mobile and ubiquitous computing both as a goal in itself and as an intermediate task in the design of advanced applications. Virtually all existing activity recognition systems for mobile phones base their predictions on location cues. This approach forces the user to disclose personal information such as her home or work area. In this paper, we present a novel activity recognition system called TRAcME (temporal recognition of activities for mobile environments) which recognises generic human activities from large windows of context, Allenpsilas temporal relations and anonymous landmarks. Unlike existing systems, TRAcME handles simultaneous activities and outputs activities which are consistent with each other at the scale of a userpsilas day.