|Table of Contents|

[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()
空巢老人智能监护系统中异常检测问题
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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
空巢老人智能监护系统中异常检测问题
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
杨蕾1;  杨路明1;  满君丰1;  2;  刘广滨2
1中南大学信息科学与工程学院, 长沙 410083; 2湖南工业大学计算机与通信学院, 株洲 412008
Keywords:
multi-media ontology;  semantic annotation;  abnormality detection;  hierarchical hidden Markov model;  pessimistic emotion model
多媒体本体;  语义标注;  异常检测;  分层隐马尔科夫模型;  悲观情感模型
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.
为实现空巢家庭内老人和家用设备异常行为的实时预测, 用多模态传感器获取行为的离散动作序列, 并用改进的多层隐马科夫模型抽象出人的高层行为——事件, 从大量的时空数据中形成描述居住着正常行为的结构化表达模型, 这些模型用作检测居住者异常行为的分类器.为表达推理预测所需的环境上下文信息, 设计了多媒体本体(MMO)来标注和推理智能监护系统中的媒体信息.改进了一种悲观情感模型(PEM)来分析室内多活动设备的多交叉事件.实验证明, 当被检测的设备处于盲区或被遮挡的情况下, PEM能增强对活动设备检测的准确性和可靠性, 上述方法在异常的实时检测方面有很好的性能.

References:

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[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.
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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.
Last Update: 2008-09-20