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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [27 references]  Display  Hide  Download

https://hal.inria.fr/hal-00953512
Contributor : Michel Vacher <>
Submitted on : Friday, February 28, 2014 - 12:03:37 PM
Last modification on : Thursday, October 17, 2019 - 12:35:01 PM
Long-term archiving on : Friday, May 30, 2014 - 3:25:35 PM

File

2012_AMI_Chahuara.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00953512, version 1

Citation

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. pp.177--192. ⟨hal-00953512⟩

Share

Metrics

Record views

544

Files downloads

528