[1] Yang Lei, Yang Luming, Man Junfeng, Liu Guangbin, et al. Detecting abnormalities for empty nest elder in smart monitoring system [J]. Journal of Southeast University (English Edition), 2008, 24 (3): 347-350. [doi:10.3969/j.issn.1003-7985.2008.03.023]

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Detecting abnormalities for empty nest elder in smart monitoring system()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

24

Issue:

2008 3

Page:

347-350

Research Field:

Computer Science and Engineering

Publishing date:

2008-09-30

Info

Title:

Detecting abnormalities for empty nest elder in smart monitoring system

Author(s):

Yang Lei¹;  Yang Luming¹;  Man Junfeng¹;  2;  Liu Guangbin²

¹College of Information Science and Technology, Central South University, Changsha 410083, China
²College of Computer and Communication, Hunan University of Technology, Zhuzhou 412008, China

Keywords:

multi-media ontology;  semantic annotation;  abnormality detection;  hierarchical hidden Markov model;  pessimistic emotion model

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2008.03.023

Abstract:

In order to implement the real-time detection of abnormality of elder and devices in an empty nest home, multi-modal joint sensors are used to collect discrete action sequences of behavior, and the improved hierarchical hidden Markov model is adopted to abstract these discrete action sequences captured by multi-modal joint sensors into an occupant’s high-level behavior—event, then structure representation models of occupant normality are modeled from large amounts of spatio-temporal data. These models are used as classifiers of normality to detect an occupant’s abnormal behavior.In order to express context information needed by reasoning and detection, multi-media ontology(MMO)is designed to annotate and reason about the media information in the smart monitoring system.A pessimistic emotion model(PEM)is improved to analyze multi-interleaving events of multi-active devices in the home.Experiments demonstrate that the PEM can enhance the accuracy and reliability for detecting active devices when these devices are in blind regions or are occlusive. The above approach has good performance in detecting abnormalities involving occupants and devices in a real-time way.

References:

[1] Kijak E, Oisel L, Gros P.Hierarchical structure analysis of sport videos using HMMs[C]//International Conference on Image Processing.Barcelona, Spain, 2003:1025-1028.
[2] Haussler David.The hierarchical hidden Markov model:analysis and applications [J].Machine Learning, 1998, 32(1):41-62.
[3] Nguyen N T, Venkatesh S, Bui H.Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association [C]//BMVC2006.Edinburgh, 2006:1229-1239.
[4] Nguyen N T, Phung D Q, Venkatesh S, et al.Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model [C]//Proc IEEE Conf Computer Vision and Pattern Recognition.San Diego, CA, USA, 2005:955-960.
[5] Moncrieff Simon, Venkatesh S, West Geoff, et al.Multi-modal emotive computing in a smart house environment [J].Pervasive and Mobile Computing, 2007, 3(2):74-94.
[6] Jank Wolfgang.The EM algorithm, its stochastic implementation and global optimization:some challenges and opportunities for OR [J].Computer Science Interface, 2006, 36(3):367-392.

Memo

Memo:

Biographies: Yang Lei(1986—), female, graduate;Yang Luming(corresponding author), male, professor, yang@mail.csu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.60773110), the Youth Education Fund of Hunan Province(No.07B014).
Citation: Yang Lei, Yang Luming, Man Junfeng, et al.Detecting abnormalities for empty nest elder in smart monitoring system[J].Journal of Southeast University(English Edition), 2008, 24(3):347-350.

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