空巢老人智能监护系统中异常检测问题

摘要

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.