CAJ | 학술논문

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.
위실현공소가정내노인화가용설비이상행위적실시예측,용다모태전감기획취행위적리산동작서렬,병용개진적다층은마과부모형추상출인적고층행위——사건,종대량적시공수거중형성묘술거주착정상행위적결구화표체모형,저사모형용작검측거주자이상행위적분류기.위표체추리예측소수적배경상하문신식,설계료다매체본체(MMO)래표주화추리지능감호계통중적매체신식.개진료일충비관정감모형(PEM)래분석실내다활동설비적다교차사건.실험증명,당피검측적설비처우맹구혹피차당적정황하,PEM능증강대활동설비검측적준학성화가고성,상술방법재이상적실시검측방면유흔호적성능.
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.