COSAR: hybrid reasoning for context-aware activity recognition

Human activity recognition is a challenging problem for context-aware systems and applications. Research in this field has mainly adopted techniques based on supervised learning algorithms, but these systems suffer from scalability issues with respect to the number of considered activities and contextual data. In this paper, we propose a solution based on the use of ontologies and ontological reasoning combined with statistical inferencing. Structured symbolic knowledge about the environment surrounding the user allows the recognition system to infer which activities among the candidates identified by statistical methods are more likely to be the actual activity that the user is performing. Ontological reasoning is also integrated with statistical methods to recognize complex activities that cannot be derived by statistical methods alone. The effectiveness of the proposed technique is supported by experiments with a complete implementation of the system using commercially available sensors and an Android-based handheld device as the host for the main activity recognition module.

[1]  Claudio Bettini,et al.  Composition and Generalization of Context Data for Privacy Preservation , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[2]  S. Cessie,et al.  Ridge Estimators in Logistic Regression , 1992 .

[3]  Claudio Bettini,et al.  Context-Aware Activity Recognition through a Combination of Ontological and Statistical Reasoning , 2009, UIC.

[4]  Zhiyong Yu,et al.  Capture, recognition, and visualization of human semantic interactions in meetings , 2010, 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[5]  Claudio Bettini,et al.  MIMOSA: context-aware adaptation for ubiquitous web access , 2009, Personal and Ubiquitous Computing.

[6]  Diego Calvanese,et al.  The Description Logic Handbook: Theory, Implementation, and Applications , 2003, Description Logic Handbook.

[7]  Chris D. Nugent,et al.  Evidential fusion of sensor data for activity recognition in smart homes , 2009, Pervasive Mob. Comput..

[8]  Blake Hannaford,et al.  A Hybrid Discriminative/Generative Approach for Modeling Human Activities , 2005, IJCAI.

[9]  Jesús Favela,et al.  Activity-Aware Computing for Healthcare , 2008, IEEE Pervasive Computing.

[10]  Xingshe Zhou,et al.  Inferring Human Interactions in Meetings: A Multimodal Approach , 2009, UIC.

[11]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[12]  Henry A. Kautz,et al.  Location-Based Activity Recognition using Relational Markov Networks , 2005, IJCAI.

[13]  Harry Chen,et al.  Semantic Web in the context broker architecture , 2004, Second IEEE Annual Conference on Pervasive Computing and Communications, 2004. Proceedings of the.

[14]  Bernt Schiele,et al.  Towards Less Supervision in Activity Recognition from Wearable Sensors , 2006, 2006 10th IEEE International Symposium on Wearable Computers.

[15]  Matthai Philipose,et al.  Common Sense Based Joint Training of Human Activity Recognizers , 2007, IJCAI.

[16]  Bernt Schiele,et al.  ADL recognition based on the combination of RFID and accelerometer sensing , 2008, 2008 Second International Conference on Pervasive Computing Technologies for Healthcare.

[17]  Jane Yung-jen Hsu,et al.  The Diet-Aware Dining Table: Observing Dietary Behaviors over a Tabletop Surface , 2006, Pervasive.

[18]  Ian Horrocks,et al.  From SHIQ and RDF to OWL: the making of a Web Ontology Language , 2003, J. Web Semant..

[19]  Oliver Brdiczka,et al.  Learning Situation Models for Providing Context-Aware Services , 2007, HCI.

[20]  Claudio Bettini,et al.  Hybrid reasoning in the CARE middleware for context awareness , 2009, Int. J. Web Eng. Technol..

[21]  Neal Lesh,et al.  Indoor navigation using a diverse set of cheap, wearable sensors , 1999, Digest of Papers. Third International Symposium on Wearable Computers.

[22]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[23]  Oliver Amftand.  Recognition of dietary activity events using on-body sensors , 2008 .

[24]  Jian Lu,et al.  epSICAR: An Emerging Patterns based approach to sequential, interleaved and Concurrent Activity Recognition , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

[25]  Albrecht Schmidt,et al.  Multi-sensor Activity Context Detection for Wearable Computing , 2003, EUSAI.

[26]  Claudio Bettini,et al.  Shadow attacks on users' anonymity in pervasive computing environments , 2008, Pervasive Mob. Comput..

[27]  Eric Horvitz,et al.  Layered representations for human activity recognition , 2002, Proceedings. Fourth IEEE International Conference on Multimodal Interfaces.

[28]  Huan Liu,et al.  Chi2: feature selection and discretization of numeric attributes , 1995, Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence.