Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32579
Exploring the Activity Fabric of an Intelligent Environment with Hierarchical Hidden Markov Theory

Authors: Chiung-Hui Chen


The Internet of Things (IoT) was designed for widespread convenience. With the smart tag and the sensing network, a large quantity of dynamic information is immediately presented in the IoT. Through the internal communication and interaction, meaningful objects provide real-time services for users. Therefore, the service with appropriate decision-making has become an essential issue. Based on the science of human behavior, this study employed the environment model to record the time sequences and locations of different behaviors and adopted the probability module of the hierarchical Hidden Markov Model for the inference. The statistical analysis was conducted to achieve the following objectives: First, define user behaviors and predict the user behavior routes with the environment model to analyze user purposes. Second, construct the hierarchical Hidden Markov Model according to the logic framework, and establish the sequential intensity among behaviors to get acquainted with the use and activity fabric of the intelligent environment. Third, establish the intensity of the relation between the probability of objects’ being used and the objects. The indicator can describe the possible limitations of the mechanism. As the process is recorded in the information of the system created in this study, these data can be reused to adjust the procedure of intelligent design services.

Keywords: Behavior, big data, hierarchical Hidden Markov Model, intelligent object.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 683


[1] Amer, W., Ansari, U., & Ghafoor, A. (2009). Industrial automation using embedded systems and machine-to-machine, man-to-machine (M2M) connectivity for improved overall equipment effectiveness (OEE). In W. A. Gruver, L. O. Hall, D. Yeung, & M. Smith (Eds.), Proceedings 2009 IEEE International Conference on Systems, Man and Cybernetics, pp. 4450-4454. Piscataway, NJ: IEEE.
[2] Becker, C. & Dürr, F. (2005). On location models for ubiquitous computing, Personal and Ubiquitous Computing, Vol. 9, No. 1, pp. 20-31.
[3] Beigl, M., Zimmer, T. & Decker, C. (2002). A Location Model for Communicating and Processing of Context, Personal and Ubiquitous Computing, Vol. 6, No.5-6, pp. 341-357.
[4] Fine, S. Singer Y. & Tishby, N. (1998) The Hierarchical Hidden Markov Model Analysis and Applications, Machine Learning, Vol. 32, No. 1, pp. 41-62.
[5] Hamid, R. Maddi, S. Johnson, A. Bobick, A. Essa, I. Isbell. C. (2009). A Novel Sequence Representation for Unsupervised Analysis of Human Activities. Artificial Intelligence Journal, Vol. 14, No.173.
[6] Cappé, O., Moulines, E. & Rydén, T. (2005). Inference in Hidden Markov Models. Springer-Verlag New York, Inc. Secaucus, NJ, USA.
[7] Hitomi, T., Koji, T. & Siio, I. (2010). InPhase: evaluation of a communication system focused on happy coincidences of daily beh. In Proceedings of ACM CHI 2010 Conference on Human Factors in Computing Systems, pp.2481–2490, April 10-15, 2010, Atlanta, GA, USA.
[8] Ichiro, S. (2005). A World Model for Smart Spaces, 1st International Symposium on Ubiquitous Intelligence and Smart Worlds (UISW2005), Lecture Notes in Computer Science (LNCS), Vol.3823, pp.31-40.
[9] Ichiro, S. (2007). A location model for smart environments, Pervasive and Mobile Computing, Vol. 3, No. 2, pp. 158-179.
[10] International Telecommunication Union (ITU) (2005). ITU Internet reports 2005: The Internet of things, executive summary. Geneva: International Telecommunication Union.
[11] Kawsar, F., Fujinami, K. & Nakajima, T. (2007). A Lightweight Indoor Location Model for Sentient Artefacts using Sentient Artefacts, in Proceedings of the 2007 ACM symposium on applied computing, pp.1624-1631.
[12] Kim, H. & Han, Y. (2005). A proportional fair scheduling for multicarrier transmission systems. IEEE Communications Letters, Vol. 9, No. 3, pp. 210-212.
[13] Lee, Y. G., Choi J. W. & Lee I. J. (2006) Location Modeling for Ubiquitous Computing Based on the Spatial Information Management Technology, Journal of Asian Architecture and Building Engineering, Vol. 5, No.1, pp. 105-111.
[14] Lertlakkhanakul, J., Sangrae, D. & Choi, J. (2006). Developing a Spatial Context-Aware Building Model and System to Construct a Virtual Place, In: Jos P. van Leeuwen and Harry J.P. Timmermans (eds.) Progress in Design & Decision Support Systems in Architecture and Urban Planning, 8th International DDSS Conference, pp. 343-358, 4-7 July 2006, Eindhoven University of Technology.
[15] Mises, L. V. (1999). Chinese translation by T. P. Hsia, revised by H. L. Wu, “Human Action,” Taipei, Taiwan: Yuan Liu Publishing, Vol. 2, pp. 1-506.
[16] Ricquebourg, V., Menga, D., Durand, D., Marhic, B., Delahoche, L., & Logé, C. (2006). The smart home concept: Our immediate future. In Institute of Electrical and Electronics Engineers (Eds.), Proceedings of the 1st IEEE International Conference on E-Learning in Industrial Electronics, pp. 23-24. Piscataway, NJ: IEEE.
[17] Shibuya, H. (2006). Human Actions under Uncertainty: Probability as Extended Logic, Otaru University of Commerce, mimeo.
[18] Yang, J. Xu, Y. & Chen, C.S. (1997). Human action learning via Hidden Markov Model, IEEE Trans. on System, Man, and Cybernetics, Vol. 27, No. 1, pp. 34-44.