Abstract: The important and difficult problem in smart home monitoring system is to detect abnormal activities of empty nest elder or devices and give an alarm in time.Multi-Model Joint Sensors（MJS） technique is used to capture the spatio-temporal context，which is processed by all kinds of perceptual components to obtain the action of occupants.Improved Hierarchical Hidden Markov Model（HHMM） is used to abstract discrete action into human’s high-level behavior—event.The representation models for occupant normality are constructed from large number of spatio-temporal data and used as classifiers that recognize normality or abnormality，to detect occupant’s abnormal behavior.In order to describe context information，a set of new Multi-Media Ontology （MMO） is designed by utilizing the method of semantic-layering and semantic-abstracting step by step，and used to annotate and reason about media information in smart monitoring system.A Pessimistic Emotion Mode（PEM） is improved，which is used to analyze multi-interleaving event of multi-active devices in home，to solve the problem that video component has difficulty in detecting state change of active devices.The experiences demonstrate that the solution has good performance in detecting abnormality and sending instant alarms.

Key words: spatio-temporal context, Multi-Media Ontology（MMO）, semantic-layering, semantic annotation, abnormality detection, Hierarchical Hidden Markov Model（HHMM）, Pessimistic Emotion Model（PEM）

摘要： 对空巢家庭的老人和设备的异常检测与即时预警是智能家居系统的重点和难点问题。利用多模态联合传感技术获取时空上下文信息，由各类感知组件进行处理而获取居住者的动作，用改进的多层隐马尔科夫模型对离散的动作进行抽象而获得人的高层行为——事件。居住者常态的表示模型被构建并作为行为正常与否的分类器来检测异常行为。为了表达上下文信息，采用语义分级、逐层抽象的方法设计了一套多媒体本体，用于智能家居系统中对媒体信息的语义化标注和推理。改进了针对室内多活动设备的多交叉事件的悲观情感模型，用以解决视频组件难以检测的活动设备状态变化的问题。实验证实该方案在异常检测和预警方面有很好的性能。

关键词: 时空上下文, 多媒体本体, 语义分级, 语义标注, 异常检测, 多层隐马尔科夫模型, 悲观情感模型, ,