Kyungseo Park
Vangelis Metsis
ABSTRACT
In recent years, there is a growing interest about assisted living environments especially for the elderly who live alone, due to the increasing number of aged people. In order for them to live safe and healthy, we need to detect abnormal behavior that may cause severe and emergent situations for the elderly. In this work, we suggest a method that detects abnormal behavior using wireless sensor networks. We model an episode that is a series of events, which includes spatial and temporal information about the subject being monitored. We define a similarity scoring function that compares two episodes taking into consideration temporal aspects. We propose a way to determine a threshold to divide episodes into two groups that reduces wrong classification. Weights on individual functions that consist the similarity function are determined experimentally so that they can produce the good results in terms of area under curve in receiver operating characteristic (ROC) curve.
- J. M. Agosta, C. Diuk-Wasser, J. Chandrashekar, and C. Livadas. An adaptive anomaly detector for worm detection. In SYSML'07. Google Scholar
Digital Library
- R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In J. B. Bocca, M. Jarke, and C. Zaniolo, editors, VLDB'94. Google ScholarDigital Library
- M. Bezzi and R. Groenevelt. Towards understanding and modeling individual behavior and group dynamics. 2008.Google Scholar
- 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.Google Scholar
- 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. Google ScholarDigital Library
- T. H. Cormen, C. E. Leiserson, and R. Rivest. Introduction to Algorithms. 1990. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- A. M. Faizal, M. M. Zaki, S. Shahrin, Y. Robiah, and S. S. Rahayu. Threshold verification technique for network intrusion detection system, June 2009.Google Scholar
- 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. Google ScholarDigital Library
- 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.Google Scholar
- J. He, H. Li, and J. Tan. Real-time daily activity classification with wireless sensor networks using hidden markov model. In EMBS '07.Google Scholar
- S. Lühr, S. Venkatesh, G. West, and H. H. Bui. Duration abnormality detection in sequences of human activity. Technical Report TR-2004/02, Curtin University of Technology, 2004.Google Scholar
- S. Lühr, 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. Google ScholarDigital Library
- D. Lymberopoulos, A. Bamis, and A. Savvides. Extracting spatiotemporal human activity patterns in assisted living using a home sensor network. In PETRA '08. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
Index Terms
- Abnormal human behavioral pattern detection in assisted living environments
Recommendations
Human behavioral detection and data cleaning in assisted living environment using wireless sensor networks
PETRA '09: Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive EnvironmentsDue to the increasing number of the elderly, more and more people need to have additional health care such as medical or environmental monitoring information at home or nursing facility. Most elderly people are likely to have a sudden behavioral changes ...
Behavioral Patterns of Older Adults in Assisted Living
In this paper, we examine at-home activity rhythms and present a dozen of behavioral patterns obtained from an activity monitoring pilot study of 22 residents in an assisted living setting with four case studies. Established behavioral patterns have ...
Gestures in assisted living environments
GW'11: Proceedings of the 9th international conference on Gesture and Sign Language in Human-Computer Interaction and Embodied CommunicationThis paper is concerned with multimodal assisted living environments, particularly based on gesture interaction. The research of ambient assisted living is about the provision of a safe, comfortable, and inde-pendent lifestyle at a domestic environment. ...
Comments