TextToHBM: A Generalised Approach to Learning Models of Human Behaviour for Activity Recognition from Textual Instructions

There are various knowledge-based activity recognition approaches that rely on manual definition of rules to describe user behaviour. These rules are later used to generate computational models of human behaviour that are able to reason about the user behaviour based on sensor observations. One problem with these approaches is that the manual rule definition is time consuming and error prone process. To address this problem, in this paper we outline an approach that learns the model structure from textual sources and later optimises it based on observations. The approach includes extracting the model elements and generating rules from textual instructions. It then learns the optimal model structure based on observations in the form of manually created plans and sensor data. The learned model can then be used to recognise the behaviour of users during their daily activities. We illustrate the approach with an example from the cooking domain.