Activity Recognition for Smart Building Application Using Complex Event Processing Approach

Abstract

Activity recognition has become one of the most interesting and challenging subjects in performing surveillance or monitoring of smart building system. Although there are several systems already available in the market, limitations and several unresolved issues remain, especially when it involves complex engineering applications. As such, activity recognition is purposely incorporated in the smart system to detect simple and complex events that happen in the building. In all existing event detections, the complex event processing (CEP) approach has been used for the detection of complex events. The CEP is capable of abstracting meaningful events from various and heterogeneous data sources, filtering and processing both simple and complex events, as well as, producing fast mitigation action based on specific scenarios. The work reported in this paper intends to explain in detail on the development of activity recognition application using CAISER™ and NESPER© platform as well as the complex event detection that uses the CEP approach. In assessing the system performance, Matthew Coefficient Correlation (MCC) has been used as the main performance parameter.  Results obtained showed that the Temporal Constraint Template Match Detector (TCD) is more accurate, stable and better in complex event detection compared to NESPER© detector