A unifying framework for situation identification methodologies

Situation identification methodologies in pervasive computing aim to abstract low level context data into more meaningful high level contexts for use by context-aware application developers and users. Many situation identification techniques have been developed and successfully applied to limited scenarios. It is not possible to apply a single technique to a wide range of diverse applications. An ideal solution would allow the combination and and interchanging of techniques as appropriate. We propose a unifying framework for existing situation identification methodologies that will automatically select the best techniques for an application.

[1]  Gregory D. Abowd,et al.  Towards a Better Understanding of Context and Context-Awareness , 1999, HUC.

[2]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[3]  Hani Hagras,et al.  Creating an ambient-intelligence environment using embedded agents , 2004, IEEE Intelligent Systems.

[4]  Gwenn Englebienne,et al.  Accurate activity recognition in a home setting , 2008, UbiComp.

[5]  Jennifer Healey,et al.  A Long-Term Evaluation of Sensing Modalities for Activity Recognition , 2007, UbiComp.

[6]  Patrik Floréen,et al.  A Framework for Context Reasoning Systems , 2005, IASTED Conf. on Software Engineering.

[7]  Waltenegus Dargie,et al.  The Role of Probabilistic Schemes in Multisensor Context-Awareness , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW'07).

[8]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[9]  Young-Koo Lee,et al.  Modeling and reasoning about uncertainty in context-aware systems , 2005, IEEE International Conference on e-Business Engineering (ICEBE'05).

[10]  Roy H. Campbell,et al.  A Middleware for Context-Aware Agents in Ubiquitous Computing Environments , 2003, Middleware.

[11]  Simon A. Dobson,et al.  Whole-System Programming of Adaptive Ambient Intelligence , 2007, HCI.

[12]  Simon A. Dobson,et al.  Using Ontologies in Case-Based Activity Recognition , 2010, FLAIRS.

[13]  Anders Kofod-Petersen,et al.  Explanations and Case-Based Reasoning in Ambient Intelligent Systems , 2007, CaCoA.

[14]  Juan Ye,et al.  Dealing with Activities with Diffuse Boundaries , 2010 .

[15]  Sungyoung Lee,et al.  Developing Context-Aware Ubiquitous Computing Systems with a Unified Middleware Framework , 2004, EUC.

[16]  Zhiquan Wang,et al.  Recognition of human activities using SVM multi-class classifier , 2010, Pattern Recognit. Lett..

[17]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[18]  Patrik Floréen,et al.  A Framework for Distributed Activity Recognition in Ubiquitous Systems , 2005, IC-AI.

[19]  Agnar Aamodt,et al.  Case-Based Reasoning for Situation-Aware Ambient Intelligence: A Hospital Ward Evaluation Study , 2009, ICCBR.

[20]  Ted Kremenek,et al.  A Probabilistic Room Location Service for Wireless Networked Environments , 2001, UbiComp.

[21]  C. Randell,et al.  Context awareness by analysing accelerometer data , 2000, Digest of Papers. Fourth International Symposium on Wearable Computers.

[22]  Tapio Seppänen,et al.  Bayesian approach to sensor-based context awareness , 2003, Personal and Ubiquitous Computing.

[23]  James H. Aylor,et al.  Computer for the 21st Century , 1999, Computer.