A Tool Chain for a Lightweight, Robust and Uncertainty-based Context Classification System (CCS)

In this paper we present a tool chain developed to support a Context Classification System (CCS). The CCS is especially designed to run even on lightweight commodity phones with a high detection rate and low calculation effort. The main design aspect considered while building the tool chain was the support for most common users and not only developers. Moreover, the CCS we are using proves to be a robust context recognition method, which supports the gain of both context classes and a fuzzy uncertainty value describing the confidence of their classification.

[1]  Michael Beigl,et al.  Increased Robustness in Context Detection and Reasoning Using Uncertainty Measures: Concept and Application , 2009, AmI.

[2]  Steven G. Johnson,et al.  The Fastest Fourier Transform in the West , 1997 .

[3]  Kristof Van Laerhoven,et al.  Gath-Geva specification and genetic generalization of Takagi-Sugeno-Kang fuzzy models , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[4]  S. Chiu.  Method and software for extracting fuzzy classification rules by subtractive clustering , 1996, Proceedings of North American Fuzzy Information Processing.

[5]  Paul Lukowicz,et al.  Distributed Modular Toolbox for Multi-modal Context Recognition , 2006, ARCS.

[6]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[7]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[8]  Michael Beigl,et al.  Using a Context Quality Measure for Improving Smart Appliances , 2007, 27th International Conference on Distributed Computing Systems Workshops (ICDCSW'07).

[9]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[10]  Albrecht Schmidt,et al.  Advanced Interaction in Context , 1999, HUC.

[11]  David Wetherall,et al.  Recognizing daily activities with RFID-based sensors , 2009, UbiComp.

[12]  D. Salber,et al.  The Context Toolkit : Aiding the Development of Context-Aware Applications , 2000 .

[13]  Isak Gath,et al.  Unsupervised Optimal Fuzzy Clustering , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Gregory D. Abowd,et al.  The Aware Home: A Living Laboratory for Ubiquitous Computing Research , 1999, CoBuild.

[15]  Gregory D. Abowd,et al.  The context toolkit: aiding the development of context-enabled applications , 1999, CHI '99.

[16]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[17]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[18]  Albrecht Schmidt,et al.  Mediacups: experience with design and use of computer-augmented everyday artefacts , 2001, Comput. Networks.

[19]  Kristof Van Laerhoven,et al.  Accessing and Abstracting Sensor Data for Pervasive Prototyping and Development , 2005 .

[20]  Qiang Yang,et al.  Cross-domain activity recognition , 2009, UbiComp.