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 -