Pattern Discovering for Ontology Based Activity Recognition in Multi-resident Homes

By Nguyen Duy, Son Nguyen
DOI: 10.37550/tdmu.EJS/2020.04.079

Abstract

Activity recognition is one of the preliminary steps in designing and implementing assistive services in smart homes. Such services help identify abnormality or automate events generated while occupants do as well as intend to do their desired Activities of Daily Living (ADLs) inside a smart home environment. However, most existing systems are applied for single-resident homes. Multiple people living together create additional complexity in modeling numbers of overlapping and concurrent activities. In this paper, we introduce a hybrid mechanism between ontology-based and unsupervised machine learning strategies in creating activity models used for activity recognition in the context of multi-resident homes. Comparing to related data-driven approaches, the proposed technique is technically and practically scalable to real-world scenarios due to fast training time and easy implementation. An average activity recognition rate of 95.83% on CASAS Spring dataset was achieved and the average recognition run time per operation was measured as 12.86 mili-seconds.

Keywords

activity recognition, multi-resident homes, ontology–based approaches

Full text

View PDF

References

Atallah, L., Yang, G.-Z. (2009). The use of pervasive sensing for behavior profiling—a survey. Pervasive Mob. Comput. 5(5), 447–464.

Augusto, J.C., Nakashima, H., Aghajan, H. (2010). Ambient intelligence and smart environments: a state of the art. In: Handbook of Ambient Intelligence and Smart Environments, 3–31.

Aztiria, A., Izaguirre, A., Augusto, J.C. (2010). Learning patterns in ambient intelligence environments: a survey. Artif. Intell. Rev. 34(1), 35–51. Springer, Netherlands.

Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Zhiwen, Y. (2012a). Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C 42(6), 790–808.

Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Zhiwen, Y. (2012b). Sensor-based activity
recognition. IEEE Trans. Syst. Man Cybern. Part C 42(6), 790–808

Chen, L., Nugent, C.D., Wang, H. (2012c). A knowledge-driven approach to activity recognition in smart homes. IEEE Trans. Knowl. Data Eng. 24(6), 961–974.

D Nguyen, T Le, S Nguyen (2016). A Novel Approach to Clustering Activities within Sensor Smart Homes. The International Journal of Simulation Systems, Science & Technology.

D. J. Cook, M. Schmitter-Edgecombe (2009). Assessing the quality of activities in a smart environment. Methods Inf Med, 48(5),480–485.

G Okeyo, L Chen, H Wang, R Sterritt (2010). Ontology-enabled activity learning and model evolution in smart home. The International Conference on Ubiquitous Intelligence and Computing, pp. 67-82.

IA Emi, JA Stankovic (2015). SARRIMA: a smart ADL recognizer and resident identifier in multi-resident accommodations. In Proceedings of the Conference on Wireless Health (Bethesda, Maryland — October 14 - 16, 2015). ISBN: 978-1-4503-3851-6

J Ye, G Stevenson, S Dobson (2015). KCAR: A knowledge-driven approach for concurrent activity recognition. Pervasive and Mobile Computing (May 2015), vol 15, 47-70.

Jiawei Han, Micheline Kamber, and Jian Pei (2012), Mining Frequent Patterns, Associations and Correlations: Basic Concepts and Methods, in Data Mining: Concepts and Techniques, 3rd edition, 243 – 278.

KS Gayathri, KS Easwarakumar, S Elias (2017). Contextual Pattern Clustering for Ontology Based Activity Recognition in Smart Home. The International Conference on Intelligent Information Technologies (17 December 2017)

KS Gayathri, S Elias, S Shivashankar (2014). An Ontology and Pattern Clustering Approach for Activity Recognition in Smart Environments. In Proceedings of Advances in Intelligent Systems and Computing (04 March 2014)

Lotfi, A., Langensiepen, C.S., Mahmoud, S.M., Akhlaghinia, M.J. 2012: Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour. J. Ambient Intell. Humaniz. Comput. 3(3), 205–218

Rashidi, P., Cook, D.J., Holder, L.B., Schmitter-Edgecombe, M. J (2011): Discovering activities to recognize and track in a smart environment. IEEE Trans. Knowl. Data Eng. 23(4), 527–539.

T Le, D Nguyen, S Nguyen (2016). An approach of using in-home contexts for activity recognition and forecast. In Proceedings of the 2nd International Conference on Control, Automation and Robotics, ISBN: 978-1-4673-8702-6, pp. 182-186