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Modeling Human Activities Using Behaviour Trees in Smart Homes

Published: 26 June 2018 Publication History

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.

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  • (2024)Generating and Evaluating Data of Daily Activities with an Autonomous Agent in a Virtual Smart HomeACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366533121:1(1-25)Online publication date: 23-Dec-2024
  • (2024)An Open and Flexible Robot Perception Framework for Mobile Manipulation Tasks2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610743(17445-17451)Online publication date: 13-May-2024
  • (2023)A Novel Approach for Contextual Clustering and Retrieval of Behavior Trees to Enrich the Behavior of Social Intelligent AgentsElectronics10.3390/electronics1204097012:4(970)Online publication date: 15-Feb-2023
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cover image ACM Other conferences
PETRA '18: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference
June 2018
591 pages
ISBN:9781450363907
DOI:10.1145/3197768
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • NSF: National Science Foundation

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New York, NY, United States

Publication History

Published: 26 June 2018

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Author Tags

  1. Activities Modelling
  2. Aging people
  3. Assistive Technology
  4. Behaviour Tress
  5. Simulation
  6. Smart Homes

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Cited By

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  • (2024)Generating and Evaluating Data of Daily Activities with an Autonomous Agent in a Virtual Smart HomeACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366533121:1(1-25)Online publication date: 23-Dec-2024
  • (2024)An Open and Flexible Robot Perception Framework for Mobile Manipulation Tasks2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610743(17445-17451)Online publication date: 13-May-2024
  • (2023)A Novel Approach for Contextual Clustering and Retrieval of Behavior Trees to Enrich the Behavior of Social Intelligent AgentsElectronics10.3390/electronics1204097012:4(970)Online publication date: 15-Feb-2023
  • (2023)CBR Driven Interactive Explainable AICase-Based Reasoning Research and Development10.1007/978-3-031-40177-0_11(169-184)Online publication date: 30-Jul-2023
  • (2022)A survey of Behavior Trees in robotics and AIRobotics and Autonomous Systems10.1016/j.robot.2022.104096154:COnline publication date: 1-Aug-2022
  • (2021)Retrieval of behavior trees using map-and-reduce techniqueEgyptian Informatics Journal10.1016/j.eij.2021.05.005Online publication date: Jun-2021
  • (2020)SISG4HEI_Alpha: alpha version of simulated indoor scenario generator for houses with elderly individualsJournal of Building Engineering10.1016/j.jobe.2020.101963(101963)Online publication date: Nov-2020
  • (2020)Modeling the behavior of persons with mild cognitive impairment or Alzheimer’s for intelligent environment simulationUser Modeling and User-Adapted Interaction10.1007/s11257-020-09266-4Online publication date: 20-Jun-2020
  • (2020)Modeling, learning, and simulating human activities of daily living with behavior treesKnowledge and Information Systems10.1007/s10115-020-01476-xOnline publication date: 1-Jun-2020
  • (2020)Automatic Daily Activity Schedule Planning for Simulating Smart House with Elderly People Living AloneThe Impact of Digital Technologies on Public Health in Developed and Developing Countries10.1007/978-3-030-51517-1_14(171-183)Online publication date: 23-Jun-2020
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