A New Habit Pattern Learning Scheme in Smart Home

Pingquan  Wang; Hong Luo; Xinming Li; Zhongwen Zhao

doi:10.6180/jase.2016.19.1.10

A New Habit Pattern Learning Scheme in Smart Home

Computer Science and Information Engineering

Pingquan Wang^1,2, Hong Luo This email address is being protected from spambots. You need JavaScript enabled to view it.¹, Xinming Li³ and Zhongwen Zhao³

¹Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, P.R. China
²Huh-hot University for Nationalities, Huh-hot, P.R. China
³The Science and Technology on Complex Electronic System Simulation Laboratory, Academy of Equipment, Beijing, P.R. China

Received: July 23, 2015
Accepted: September 2, 2015
Publication Date: March 1, 2016

Download Citation: ||https://doi.org/10.6180/jase.2016.19.1.10

ABSTRACT

Most of the user’s activities are consistent with their habits, therefore in this paper, we propose a new habit pattern learning scheme in smart home to better obtain user’s behavior regulations and habits, which can make the home more intellectually interact with people. By recording the operations on each electric appliance in the form of time series, we firstly find out that the habit can be classified into fixed-length habit and timing habit. Then, we propose habit extraction methods based on the corresponding activity probability and calculation formulas of the habit strength. Since different habits have different variation characteristics on habit strength and time zone, we further propose the self-learning algorithms on time zone and habit strength threshold to obtain the suitable parameters. Furthermore, by defining the association among individual habits into selection, parallel, sequence, cross and inclusion, we can obtain habit set which is a group of habits with inner correlation. In order to adapt to the habit variations, we introduce a habit change factor into the habit pattern discovery algorithm so as to follow the habit changes. Finally, we construct the experimental environment in a real smart home, analyze and calculate the operation records of electric appliances in two months. The experiment results show that the proposed habit pattern learning scheme is effective and efficient.

Keywords: Smart Home, Habit Pattern, Time Series, Activity Recognition

REFERENCES

[1] Wood, W., Tam, L. and Witt, M. G., “Changing Circumstances, Disrupting Habits,” Journal of Personality and Social Psychology, Vol. 88, No. 6, p. 918 (2005). doi: 10.1037/0022-3514.88.6.918
[2] Logan, B., Healey, J., Philipose, M., Tapia, E. M. and Intille, S., A Long-term Evaluation of Sensing Modalities for Activity Recognition, Springer (2007).
[3] Helal, S., Mann, W., El-Zabadani, H., King, J., Kaddoura, Y. and Jansen, E., “The Gator Tech Smart House: A Programmable Pervasive Space,” Computer, Vol. 38, No. 3, pp. 5060 (2005). doi: 10.1109/MC.2005. 107
[4] Wood, W., Quinn, J. M. and Kashy, D. A., “Habits in Everyday Life: thought, Emotion and Action,” Journal of Personality and Social Psychology, Vol. 83, No. 6, p. 1281 (2002). doi: 10.1037//0022-3514.83.6.1281
[5] Rashidi, P. and Cook, D. J., “Keeping the Resident in the Loop: Adapting the Smart Home to the User,” Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, Vol. 39, No. 5, pp. 949 959 (2009). doi: 10.1109/TSMCA.2009.2025137
[6] Harper, R., et al., Inside the Smart Home, Springer, Vol. 1 (2003). doi: 10.1007/b97527
[7] Alam, M. R., Reaz, M. B. I. and Ali, M. A. M., A Review of Smart Homes past, Present and Future,” Systems, Man and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, Vol. 42, No. 6, pp. 11901203 (2012). doi: 10.1109/TSMCC.2012.218 9204
[8] Lutolf, R., “Smart Home Concept and the Integration of Energy Meters into a Home Based System,” Metering Apparatus and Tariffs for Electricity Supply, 1992, Seventh International Conference on, pp. 277278 (1992).
[9] Chiang, Y.-T., Hsu, J.-J., Lu, C.-H. and Fu, L.-C., “Interaction Models for Multiple-resident Activity Recognition in a Smart Home,” Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, pp. 37533758. IEEE, (2010). doi: 10.1109/ IROS.2010.5650340
[10] Wang, P. Q., Luo, H. and Yan, S., Mapping Model of Multi-identifers Oriented to Internet of Things,” SCIENTIA SINICA Informationis, Vol. 43, No. 10, pp. 12441264 (2013). doi: 10.1360/112013-126
[11] Cook, D. J., Crandall, A. S., Thomas, B. L. and Krishnan, N. C., “Casas: A Smart Home in a Box,” Computer, Vol. 46, No. 7 (2013). doi: 10.1109/MC.2012. 328
[12] Zhang, S., McClean, S. I. and Scotney, B. W., “Probabilistic Learning from Incomplete Data for Recognition of Activities of Daily Living in Smart Homes,” Information Technology in Biomedicine, IEEE Transactions on, Vol. 16, No. 3, pp. 454462 (2012). doi: 10. 1109/TITB.2012.2188534
[13] Fatima, I., Fahim, M., 346 Lee, Y.-K. and Lee, S., “Classifier Ensemble Optimization For Human Activity Recognition in Smart Homes,” Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication, p. 83. ACM (2013). doi: 10.1145/2448556.2448639
[14] Roy, A., Das, S. K. and Basu, K., “A Predictive Framework for Location-aware Resource Management in Smart Homes,” Mobile Computing, IEEE Transactions on, Vol. 6, No. 11, pp. 12701283 (2007). doi: 10.1109/ TMC.2007.1058
[15] Wttenbraker, J., Gibbs, B. L. and Kahle, L. R., “Seat Belt Attitudes, Habits, and Behaviors: An Adaptive Amendment to the Fishbein Model,” Journal of Applied Social Psychology, Vol. 13, No. 5, pp. 406421 (1983). doi: 10.1111/j.1559-1816.1983.tb01748.x
[16] Alam, M. R., Reaz, M. B. I. and Ali, M., “Speed: An Inhabitant Activity Prediction Algorithm for Smart Homes,” Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, Vol. 42, No. 4, pp. 985990 (2012). doi: 10.1109/TSMCA.2011.21 73568