Abstract
The abnormal activity detection in smart environments has experienced increasing attention over years, due to its usefulness in pervasive applications. In order to meet the real-time needs and overcome the high costs and privacy issues, this paper proposes distributed abnormal activity detection approach ( DetectingAct), which employs the computing and storage resources of simple and ubiquitous sensor nodes, to detect abnormal activity in smart environments equipped with wireless sensor networks (WSN). In DetectingAct, activity is defined as the combination of trajectory and duration, and abnormal activity is defined as the activity which deviates greater enough from those normal activities. DetectingAct works as follows. Firstly, DetectingAct finds the normal activity patterns through duration-dependent frequent pattern mining algorithm (DFPMA), which adopts unsupervised learning instead of supervised learning. Secondly, the distributed knowledge storage mechanism (DKSM) is introduced to store the mined patterns in each node. Then, the current triggered sensor adopts distributed abnormal activity detection algorithm (DAADA), in which the clustering analysis plays a critical role, to compare the present activity with normal activity patterns, by calculating the similarity between them. The feasibility, real-time property, and accuracy of the DetectingAct algorithm are evaluated using both simulation and real experiments case studies.
Highlights
User activity detection is one of the key applications in smart environments
Due to the importance of using it to monitor signature or suspicious activities, the abnormal activity detection can be applied to various application domains ranging from the smart home for healthcare to the intelligent building for security, as mentioned later in details
Whether the activity is normal can be more efficiently reflected by the combination of trajectory and duration
Summary
User activity detection is one of the key applications in smart environments. As detecting users’ normal activities is interesting and important, it has attracted attention of a lot of researchers. The abnormal activity detection has great significance in building pervasive and smart environments applications. Due to the importance of using it to monitor signature or suspicious activities, the abnormal activity detection can be applied to various application domains ranging from the smart home for healthcare to the intelligent building for security, as mentioned later in details. In elderly care assisted living, a healthy old person in home has a regular routine, with almost regular activity trajectories patterns. Once he or she feels physical discomfort or physical state declining, the abnormality of the activity trajectory or the routine (e.g., more or fewer trips to the bathroom) will be detected. In the elderly care, a person staying at a location for a longer duration than usual might indicate the onset of illness and need to alert a family member or formal care provider
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