Modeling Human Activities Using Behaviour Trees in Smart Homes

Abstract

With the aging population, researchers around the world are investigating technological solutions to help seniors stay at home as long as possible. One of them is the concept of smart home, which is an intelligent house equipped with sensors and actuators. Aging people often suffers from physical and cognitive impairments, which limit their abilities to perform their Activities of Daily Living (ADL). Therefore, the smart home needs to be able to assist its resident in carrying out their ADL, when it is required. Recognising the ongoing ADL constitutes then a key challenge of the assistive services. Being able to simulate users' behaviour is also an important issue, as well as being able to find an assistive step-by-step solution when something goes wrong. However, all theses challenges need to rely on a knowledge base of activities' models. In the past, many researchers tried to make use of some logical encoding of the activities by exploiting, for instance, first order logic. These approaches work fine for the inferential process but they are very rigid, complex and time consuming. More recently, scientists in the field tried to represent the activities using stochastic models, such as Bayesian Networks or Markov Model. These probabilistic methods do not represent activities very naturally and are very static state-transition models. In this paper, we propose the use of Behaviour Trees (BT) as a means to represent the user's ADL in a smart home. BTs are mainly used in the video game industry as a powerful tool to model the behaviour of non-player characters. BTs allow the modelling of activities with a flexible, well-defined approach. We will present a first exploitation of the behaviour trees in a smart home simulator.