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State-Space based Linear Modeling for Human Activity Recognition in Smart Space



Recognition of human activity is a key element for building intelligent and pervasive environments. Inhabitants interact with several objects and devices while performing any activity. Interactive objects and devices convey information that can be essential factors for activity recognition. Using embedded sensors with devices or objects, it is possible to get object-use sequencing data. This approach does not create discomfort to the user than wearable sensors and has no impact or issue in terms of user privacy than image sensors. In this paper, we propose a linear model for activity recognition based on the state-space method. The activities and sensor data are considered as states and inputs respectively for linear modeling. The relationship between the states and inputs are defined by a coefficient matrix. This model is flexible in terms of control because all the elements are represented by matrix elements. Three real datasets are used to compare the recognition accuracy of the proposed method to those of other well-known activity recognition model to validate the proposed model. The results indicate that the proposed model achieves a significantly better recognition performance than other models.



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M. Arif, A. Kattan, and S. I. Ahamed, (2017). Classification of physical activities using wearable sensors, Intelligent Automation & Soft Computing, 23(1), 21-33.

P. Augustyniak, M. Smoleń, Z. Mikrut, and E. Kańtoch, (2014). Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors, Sensors, 14, 7831-7856.

W. W. Chen, (1995). Fast Effective Rule Induction, In Proceedings of the Twelfth International Conference on Machine Learning, 9-12,115-123.

D. J. Cook, (2012). Learning Setting-Generalized Activity Models for Smart Spaces, IEEE Intelligent Systems, 27(1), 32-38.

D. J. Cook, N. C. Krishman, and P. Rashidi, (2013). Activity discovery and activity recognition: a new partnership, IEEE Transactions on Cybernetics, 43(3), 820-828.

I. Fatima, M. Fahim, Y.-K. Lee, and S. Lee, (2013). Analysis and effects of home dataset characteristics for daily life activity recognition, Journal of Supercomputing, 66(2), 760-780.

A Jalal, S. Kamal, and D. Kim, (2014). A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments, Sensors, 14, 11735-11759.

S. Karaman, J. B-Pineau, V. Dovgalecs, R. Mégret, J.Pinquier, R. André-Obrecht, Y. Gaëstel, and J.-F. Dartigues, (2014). Hierarchical Hidden Markov Model in detecting activities of daily living in wearable videos for studies of dementia, Multimedia Tools and Applications, 69(3), 743-771.

S.-R. Ke, H. L. U. Thuc, Y.-J.Lee, J.-N.Hwang, J.-H.Yoo, and K.-H. Choi, (2013). A Review on Video-Based Human Activity Recognition, Computers, 2, 88-131.

Ó. D. Lara & M. A. Labrador, (2012). A Survey on Human Activity Recognition using Wearable Sensors, IEEE Communications Surveys & Tutorials, 15(3), 1192-1209.

J. Li, Y. An, R. Fei, H. Wang and Q. Yan, (2017). Activity recognition method based on weighted LDA data fusion, Intelligent Automation & Soft Computing, 23(3) 509-517.

L. Ljung and T.Soderstrom, (1982). Theory and Practice of Recursive Identification, The MIT Press, Cambridge, Mass, USA.

Nazerfard, Ehsan et al. "Conditional Random Fields for Activity Recognition in Smart Environments." Proceedings of the ACM international conference on Health informatics - IHI ”10 (2010): n. pag. Crossref. Web.

W. Nor Harizon, W. Mohamed, Mohd Najib, Mohd Salleh, and Abdul Halim Omar, (2012). Comparative Study of Reduced Error Pruning Method in Decision Tree Algorithms, IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia.

F. J. Ordonez, Toledo, and A. Sanchis, (2013). Activity recognition using hybrid generative/discriminative models on home environments using binary sensors, Sensors. 13(5), 5460-5477.

P. Palmes, H. K. Pung, T.Gu, W. Xue, and S. Chen, (2010). Object relevance weight pattern mining for activity recognition and segmentation, Pervasive and Mobile Computing, 6(1), 43-57.

J.-Y. Su, S.-C. Cheng and D.-K. Huang, (2015). Unsupervised Object Modeling and Segmentation with Symmetry Detection for Human Activity Recognition, Symmetry, 7, 427-449.

Tapia, Emmanuel Munguia, Stephen S. Intille, and Kent Larson. "Activity Recognition in the Home Using Simple and Ubiquitous Sensors." Pervasive Computing (2004): 158-175. Crossref. Web.

T. L. M. Van Kasteren, (2011). Datasets for Activity Recognition,

T. L. M. Van Kasteren, G. Englebienne, and B. J. A. Kröse, (2011). Human Activity Recognition from Wireless Sensor Network Data: Benchmark and Software, Activity Recognition in Pervasive Intelligent Environments, Atlantis Press, 165-186.

T. L. M. Van Kasteren, A. Noulas, G. Englebienne, and B. J. Korse, (2007). Accurate Activity Recognition in a Home Setting, In Proceedings of the Conference on Autonomous Agents and Multiagent Systems (AAMAS2007), 1-9.

M. Vrigkas, C Nikou, and IA Kakadiaris, (2015). A Review of Human Activity Recognition Methods, Frontiers in Robotics and AI, 2, 1-28.

D. Wilson and C. Atkeson, (2005). Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors, Pervasive Computing, 3468, 62-79.

S.-H. Yang, M. H. Kabir, and M. RobiulHoque, (2016). Mathematical Modeling of Smart Space for Context-Aware System: Linear Algebraic Representation of State-Space Method based Approach, Mathematical Problems in Engineering, 2016, 1-8.

K. Yatani & K. N. Truong, (2012). Body Scope: A Wearable Acoustic Sensor for Activity Recognition, In Proceedings of the 14th International Conference on Ubiquitous Computing.


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


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