From Textual Instructions to Sensor-based Recognition of User Behaviour

Abstract

There are various activity recognition approaches that rely on manual definition of precondition-effect 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 extracts the 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.