Thu Dau Mot University Journal of Science

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

By Nguyen Duy, Son Nguyen
Published online 15/12/2020
DOI: 10.37550/tdmu.EJS/2020.04.079


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.

Full text

View PDF


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

Publication Information


Thu Dau Mot University, Viet Nam

Honorary Editor-in-Chief and Chairman of the Editorial Board

Assoc. Prof. Nguyen Van Hiep

Deputy Editor-in-Chief

PhD. Trần Hạnh Minh Phương
Thu Dau Mot University

Editorial Board

Prof. Tran Van Doan
Fujen University, Taiwan
Prof. Zafar Uddin Ahmed
Vietnam National University Ho Chi Minh City

Prof.Dr. Phillip G.Cerny
The University of Manchester, United Kingdom
Prof. Ngo Van Le
University of Social Sciences and Humanities (VNU-HCM)

Prof. Bui The Cuong
Southern Institute of Social Sciences​​​​​​​
Prof. Le Quang Tri
Can Tho University

Assoc. Prof. Nguyen Van Duc
Animal Husbandry Association of Vietnam
Assoc. Prof. Ted Yuchung Liu
National Pingtung University, Taiwan

PhD. Anita Doraisami
Economics Monash University, Australia
Prof. Dr. Andrew Seddon
Asia Pacific University of Technology & innovation (APU)

Assoc. Prof. Le Tuan Anh
Thu Dau Mot University
Prof. Abtar Darshan Singh
Asia Pacific University, Malaysia

Prof.Dr. Ron W.Edwards
The University of Melbourne, Australia
Assoc. Prof. Hoang Xuan Nien
Thu Dau Mot University

PhD. Nguyen Duc Nghia
Vietnam National University Ho Chi Minh City
PhD. Bao Dat
Monash University (Australia)

PhD. Raqib Chowdhury
Monash University (Australia)
PhD. Nguyen Hoang Tuan
Thu Dau Mot University

PhD. Nguyen Thi Lien Thuong
Thu Dau Mot University


Nguyen Thi Man
Thu Dau Mot University