On-Line Inference Comparison with Markov Logic Network Engines for Activity Recognition in AAL Environments

Abstract

We address possible solutions for a practical application of Markov Logic Networks to online activity recognition, based on domotic sensors, to be used for monitoring elderly with mild cognitive impairments. Our system has to provide responsive information about user activities throughout the day, so different inference engines are tested. We use an abstraction layer to gather information from commercial domotic sensors. Sensor events are stored using a non-relational database. Using this database, evidences are built to query a logic network about current activities. Markov Logic Networks are able to deal with uncertainty while keeping a structured knowledge. This makes them a suitable tool for ambient sensors based inference. However, in their previous application, inferences are usually made offline. Time is a relevant constrain in our system and hence logic networks are designed here accordingly. We compare in this work different engines to model a Markov Logic Network suitable for such circumstances. Results show some insights about how to design a low latency logic network and which kind of solutions should be avoided.