Using Markov Logic Network for On-line Activity Recognition from Non-Visual Home Automation Sensors

Abstract : This paper presents a Markov Logic Networks (MLN) approach for the on-line recognition of human activities in a smart home. The method recognises activity from non visual and non wearable sensors data. The classification model benefit from a logic formal representation and uses probabilistic inference to deal with uncertainty. The evaluation was carried out on a real smart home where 21 participants performed several daily activities recorded by microphones and several classical home automation sensors. The MLN approach reaches an accuracy of 85.3% while the basleine support vector machine and naive Bayes approches leads to 59.6% and 66.1% respectively. Results show not only the great abilities of MLN as a classifier but also its potential to be integrable into a formal knowledge representation framework.
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Communication dans un congrès
Ambient Intelligence, Nov 2012, Pisa, Italy. Springer (Heidelberg), 7683, pp.177--192, 2012, Lecture Notes in Computer Science
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  • HAL Id : hal-00953512, version 1

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Pedro Chahuara, Anthony Fleury, François Portet, Michel Vacher. Using Markov Logic Network for On-line Activity Recognition from Non-Visual Home Automation Sensors. Ambient Intelligence, Nov 2012, Pisa, Italy. Springer (Heidelberg), 7683, pp.177--192, 2012, Lecture Notes in Computer Science. 〈hal-00953512〉

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