Performance metrics for activity recognition
ACM Transactions on Intelligent Systems and Technology (TIST)
On-line ADL Recognition with Prior Knowledge
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
ACE: exploiting correlation for energy-efficient and continuous context sensing
Proceedings of the 10th international conference on Mobile systems, applications, and services
Semantic enrichment of mobile phone data records
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
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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, Allen’s 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 user’s day.