Using markov logic network for on-line activity recognition from non-visual home automation sensors
Ambient Intelligence: Third International Joint Conference, AmI 2012, Pisa …, 2012•Springer
This paper presents the application of Markov Logic Networks (MLN) for the the recognition
of Activities of Daily Living (ADL) in a smart home. We describe a procedure that uses raw
data from non visual and non wearable sensors in order to create a classification model
leveraging logic formal representation and probabilistic inference. SVM and Naive Bayes
methods were used as baselines to compare the performance of our implementation, as
they have proved to be highly efficient in classification tasks. The evaluation was carried out …
of Activities of Daily Living (ADL) in a smart home. We describe a procedure that uses raw
data from non visual and non wearable sensors in order to create a classification model
leveraging logic formal representation and probabilistic inference. SVM and Naive Bayes
methods were used as baselines to compare the performance of our implementation, as
they have proved to be highly efficient in classification tasks. The evaluation was carried out …
Abstract
This paper presents the application of Markov Logic Networks(MLN) for the the recognition of Activities of Daily Living (ADL) in a smart home. We describe a procedure that uses raw data from non visual and non wearable sensors in order to create a classification model leveraging logic formal representation and probabilistic inference. SVM and Naive Bayes methods were used as baselines to compare the performance of our implementation, as they have proved to be highly efficient in classification tasks. The evaluation was carried out on a real smart home where 21 participants performed ADLs. Results show not only the appreciable capacities of MLN as a classifier, but also its potential to be easily integrable into a formal knowledge representation framework.
Springer
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