Abnormal human behavioral pattern detection in assisted living environments

ACM International Conference Proceeding Series

Volumn , Issue , 2010, Pages

  Park, Kyungseo ^a   Lin, Yong ^a   Metsis, Vangelis ^a   Le, Zhengyi ^a   Makedon, Fillia ^a  

^a The University of Texas at Arlington   (United States)

Author keywords

Abnormal behavior detection; Assisted living environment; Classication; Similarity function; Threshold value

Indexed keywords

ABNORMAL BEHAVIOR; ASSISTED LIVING; CLASSICATION; SIMILARITY FUNCTIONS; THRESHOLD VALUE;

WIRELESS SENSOR NETWORKS;

EID: 77956274462     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1839294.1839305     Document Type: Conference Paper

References (17)

1. J. M. Agosta, C. Diuk-Wasser, J. Chandrashekar, and C. Livadas. An adaptive anomaly detector for worm detection. In SYSML'07.
2. R. Agrawal and R. Srikant. Fast algorithms for miningassociation rules in large databases. In J. B. Bocca,M. Jarke, and C. Zaniolo, editors, VLDB'94.
3. M. Bezzi and R. Groenevelt. Towards understanding and modeling individual behavior and group dynamics. 2008.
4. C.-S. Chao, Y.-X. Chen, and A.-C. Liu. Abnormal event detection for network flooding attacks. Journal of Information Science and Engineering, 20(6):1079-1091, 2004.
5. P.-C. Chung and C.-D. Liu. A daily behavior enabled hidden markov model for human behavior understanding. Pattern Recognition, 41(5):1572-1580, May 2008.
6. T. H. Cormen, C. E. Leiserson, and R. Rivest. Introduction to Algorithms. 1990.
7. T. V. Duong, H. H. Bui, D. Q. Phung, and S. Venkatesh. Activity recognition and abnormality detection with the switching hidden semi-markov model. In CVPR '05.
8. A. M. Faizal, M. M. Zaki, S. Shahrin, Y. Robiah, and S. S. Rahayu. Threshold verification technique for network intrusion detection system, June 2009.
9. X. Geng, G. Li, Y. Ye, Y. Tu, and H. Dai. Abnormal behavior detection for early warning of terrorist attack. In AI 2006: Advances in Artificial Intelligence, 2006.
10. P. D. Haghighi, M. M. Gaber, S. Krishnaswamy, and A. Z. S. W. Loke. Context-aware adaptive data stream mining. Special Issue on Knowledge Discovery from Data Streams, 2008.
11. J. He, H. Li, and J. Tan. Real-time daily activity classication with wireless sensor networks using hidden markov model. In EMBS '07.
12. S. LÄuhr, S. Venkatesh, G. West, and H. H. Bui. Dration abnormality detection in sequences of human activity. Technical Report TR-2004/02, CurtinUniversity of Technology, 2004.
13. S. LÄuhr, G. West, and S. Venkatesh. Recognition of emergent human behaviour in a smart home: A data mining approach. Pervasive Mob. Comput., 3(2):95-116, 2007.
14. D. Lymberopoulos, A. Bamis, and A. Savvides.Extracting spatiotemporal human activity patterns in assisted living using a home sensor network. In PETRA '08.
15. K. Park, E. Becker, J. K. Vinjumur, Z. Le, and F. Makedon. Human behavioral detection and data cleaning in assisted living environment using wirless sensor networks. In PETRA 09, 2009.
16. E. M. Tapia, S. S. Intille, and K. Larson. Activity recognition in the home using simple and ubiquitous sensors. In Pervasive Computing, pages 158-175, 2004.
17. N. Vaswani, A. R. Chowdhury, and R. Chellappa. "shape activity": A continuous state hmm for moving/deforming shapes with application to abnormal activity detection. IEEE Trans. on Image Processing, 14(10):1603-1616, 2005.