Journal of Advances in Information Technology, Vol 3, No 1 (2012), 29-35, Feb 2012
doi:10.4304/jait.3.1.29-35

Activity Recognition in Ubiquitous Learning Environment

Tao Lu, Shaokun Zhang, Qian Hao

Abstract


With the advance and the development of sensors, computing devices and wireless communication networks, ubiquitous computing has been an active and fast growing research area. The technology is applied in educational domain, resulting in ubiquitous learning, in which the system can detect the students’ behavior and provide personalized support to guide the students to learn in real world. This paper focuses on activity recognition in ubiquitous learning environment which assists the novice user to conduct a complex experiment. Here the activity is a kind of complex activity which is an independent task to achieve a certain goal and is composed of simple actions. The approach we propose is knowledge-driven technique. We first present concepts of context pattern and context evolving pattern, and based on these concepts define the activity model. Then we analyze the condition of distinguishable activity and propose the algorithm of activity recognition. Activity model in single-crystal X-ray diffraction experiment support system is designed and the method is further interpreted through this case.


Keywords


ubiquitous learning, context awareness, context reasoning, activity recognition

References


M. Weiser, “The computer for 21st century,” Scientific American, vol.261, no.30, pp.94-104, 1991.
http://dx.doi.org/10.1038/scientificamerican0991-94

H. Ogata and Y. Yano, “How Ubiquitous Computing can support language learning,” In Proceedings of KEST, pp.1-6, 2003.

G.J. Hwang, C.C. Tsai, and S.J.H. Yang, “Criteria, strategies and research issues of context-aware ubiquitous learning,” Educational Technology and Society, vol.11, no.1, pp. 81-91, 2008.

G.J. Hwang, T.C. Yang, and C.C. Tsa, “A context-aware ubiquitous learning environment for conducting complex science experiments,” Computers and Education, vol.53, no.2, pp.402-413, 2009.
http://dx.doi.org/10.1016/j.compedu.2009.02.016

H. Ogata, C. Yin, and Y. Yano, “JAMIOLAS: Supporting Japanese Mimicry and Onomatopoeia Learning with Sensors,” the Wireless, Mobile and Ubiquitous Technology in education, pp.111-115, 2006.

R. Joiner, J. Nethercott, R, and Hull, J Reid, “Designing educational experiences using ubiquitous technology,” Computers in Human Behaviors, vol.22, no.1, pp.67–76,2006.
http://dx.doi.org/10.1016/j.chb.2005.01.001

H. Ogata, and Y. Yano, “Context-aware support for computer-supported ubiquitous learning,” the 2nd IEEE International Workshop on Wireless and Mobile Technologies in Education,JhongLi, Taiwan, pp.27-34, March, 2004.

Y. Rogers, S. Price, C. Randell, and D.S. Fraser, “Ubi-learning integrating indoor and outdoor learning experiences,” Communications of the ACM, vol.48, no.1, pp.55–59, 2005.
http://dx.doi.org/10.1145/1039539.1039570

H.C. Chu, G.J. Hwang, S.X. Huang, and T.T. Wu, “A knowledge engineering approach to developing e-libraries for mobile learning,” Electronic Library, vol.26, no.3,pp.303-317, 2008.
http://dx.doi.org/10.1108/02640470810879464

C. Zhu, and W.H. Sheng, “Motion- and location-based online human daily activity recognition,” Pervasive and Mobile Computing, vol.7, no.2, pp.256-269, 2011.
http://dx.doi.org/10.1016/j.pmcj.2010.11.004

D. Riboni, and C. Bettini, “OWL 2 modeling and reasoning with complex human activities,” Pervasive and Mobile Computing, vol.7, no.3, pp.379-395, 2011.
http://dx.doi.org/10.1016/j.pmcj.2011.02.001

M.S. Ryoo, and J.K. Aggarwal, “Recognition of composite human activities through context-free grammar based representation,” IEEE Conference on Computer Vision and Pattern Recognition, New York, USA, pp.1709-1718, June 2006.

J. Lester, T. Choudhury, and N. Kern, “A hybrid discriminative/generative approach for modeling human activities,” In Proceedings of the 19th International Joint Conference on Artificial Intelligence, Professional Book Center, Acapulco,Mexico, pp. 766-772, 2005.

L. Liao, D. Fox, and H. Kautz, “Location-based activity recognition using relational Markov networks,” In Proceedings of the 19th International Joint Conference on Artificial Intelligence, Acapulco, Mexico, pp.773-778, 2005.

D.W Albrecht, and I. Zukerman, “Bayesian Models for Keyhole Plan Recognition in an Adventure Game,” User Modeling and User-Adapted Interaction, vol.8, pp.5–47,1998.
http://dx.doi.org/10.1023/A:1008238218679

E.M Tapia, and S. Instille, “Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor,” the 11th IEEE International Symposium on Wearable Computers, Boston,USA, pp. 37 – 40, October 2007.

H. Kautz, “A Formal Theory of Plan Recognition and its Implementation,” Reasoning About Plans, San Francisco: Morgan Kaufmann, 1991, pp.69-125.

R. Kowalski, and M. Sergot, “A logic-based calculus of events,” New Generation Computing, vol.4, no.1, pp.67-95, 1986.
http://dx.doi.org/10.1007/BF03037383

O. Brdiczka, J.L. Crowley, and P. Reignier, “Learning situation models for providing context-aware services,” Ambient Interaction 4th International Conference on Universal Access in Human-Computer Interaction, Beijing,China, pp.23-32, July 2007.

L. Chen, and C.D. Nugent, “Ontology-based activity recognition in intelligent pervasive environments,” International Journal of Web Information Systems, vol.5, no.4, pp.410-430, 2009.
http://dx.doi.org/10.1108/17440080911006199

L. Chen, C. Nugent, M. Mulvenna, D. Finaly, X. Hong, and M. Poland, “Using event calculus for behaviour reasoning and assistance in a smart home,” in Proceedings of the 6th International Conference on Smart Homes and Health Telematics, IA, USA, vol. 5120, pp. 81–89, June 2008.
http://dx.doi.org/10.1007/978-3-540-69916-3_10


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