Affiliations: Department of Information Technology and Computer Engineering, Safashahr Branch, Islamic Azad University, Safashahr, Iran
Correspondence:
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Corresponding author: Ali Reza Honarvar, Department of Information Technology and Computer Engineering,
Abstract: Various sensors are embedded in different places of smart environments to monitor and collect data about status of environments. The goal of a smart environment is to improve quality of life by enhancing the efficiency of services, providing residents’ needs using different technologies, and mining the captured data in the environment. Mining such data for extracting valuable knowledge requires critical activities and situations in smart environments to be effectively detected. Activity recognition is of great interest for researchers in context-awareness computing. However, correlations between activities and their frequent patterns have never been addressed by the traditional activity recognition techniques. Recently, some researchers have considered the frequent pattern extraction for activity detection in smart environments. Despite that, sequences and time durations between activities and sensors’ activation have not been scrutinized for activity recognition. In this paper, an extension of frequent pattern-based algorithms is proposed for activity recognition. This novel algorithm considers sequence of activated sensors as well as time durations between them in order to extract the frequent sequential patterns for activity/situation detection in smart environments. The experiment results using the publicly-available datasets demonstrated that the suggested method is efficient and can significantly improve accuracy of activity recognition in smart environments, considering the sequence matching-based conflict resolution and the order of the activated sensors.
Keywords: Smart environment, activity recognition, smart city analytics, sequential pattern mining, big data
