Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Inferring contexts from human activities in smart spaces
AU - Lee, J.W.
AU - Helal, Sumi
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Abstract structures
KW - Activity models
KW - Activity recognition
KW - Human activities
KW - Means clustering
KW - Model and simulation
KW - Smart space
KW - Synthetic datasets
KW - Principal component analysis
M3 - Conference contribution/Paper
SN - 9781577357568
SP - 695
EP - 700
BT - 29th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2016
A2 - Markov, Zdravko
A2 - Russell, Ingrid
PB - AAAI
CY - Palo Alto
ER -