Assistive intelligent environments for automatic health monitoring
Assistive intelligent environments for automatic health monitoring
A probabilistic reasoning framework for smart homes
Proceedings of the 5th international workshop on Middleware for pervasive and ad-hoc computing: held at the ACM/IFIP/USENIX 8th International Middleware Conference
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Structured Learning of Component Dependencies in AmI Systems
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Sensor-based understanding of daily life via large-scale use of common sense
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Unsupervised activity recognition using automatically mined common sense
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Building reliable activity models using hierarchical shrinkage and mined ontology
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
A comparison of HMMs and dynamic bayesian networks for recognizing office activities
UM'05 Proceedings of the 10th international conference on User Modeling
Pervasive and Mobile Computing
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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In this paper we present an approach to the unsupervised recognition of activities of daily living (ADLs) in the context of smart environments The developed system utilizes background domain knowledge about the user activities and the environment in combination with probabilistic reasoning methods in order to build best possible explanation of the observed stream of sensor events The main advantage over traditional methods, e.g dynamic Bayesian models, lies in the ability to deploy the solution in different environments without needing to undergo a training phase To demonstrate this, tests with recorded data sets from two ambient intelligence labs have been conducted The results show that even using basic semantic modeling of how the user behaves and how his/her behavior is reflected in the environment, it is possible to draw conclusions about the certainty and the frequencies with which certain activities are performed.