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Inferring contexts from human activities in smart spaces

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Modeling and simulation of human activities is becoming a hot research area for validating activity recognition algorithms used to generate useful synthetic datasets for assistive environments and other smart spaces. Context-driven simulation, an emerging approach that utilizes abstract structures of state spaces (contexts), can enhance the scalability and realism of simulations. However, the context-driven approach is demanding of users' efforts in specifying not only activity models, but also the corresponding contexts and contextual transitions associated with these activities. In this paper, we propose a method to reduce users' efforts in configuring simulation by using &-means clustering and principal component analysis approaches to automate the derivation of contexts from a given set of activities. We validate our approach by comparing the actual sequenced activities with the derived sequenced activities. Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All right reserved.