Semantic Smart Homes: Situation-Aware Assisted Living

  • Liming ChenEmail author
  • Chris D. Nugent


This chapter introduces a systematic approach to providing situation-aware ADL assistances using the semantic smart home environment. It first analyses the nature and issues of SH-based healthcare for cognitively deficient inhabitants, and discusses the ways in which semantic technologies enhance situation comprehension. It then presents an intelligent agent system for interpreting and reasoning semantic situational (meta)data to enhance situation-aware decision support. The Chapter drills down to provide details of semantic sensor data modelling, fusion and storage and retrieval mechanisms, which creates machine understandable and processable situational data. It also describes a cognitive agent for realizing high-level cognitive capabilities such as prediction and explanation by exploiting the generated semantic content. An example case study has been conducted using a prototype agent-based assistive system to illustrate the proposed approach through simulated and real-time ADL assistance scenarios in the context of situation aware assistive living.


  1. 1.
    Boger J, Poupart P, Hoey J, Boutilier C, Fernie G, Mihailidis A (2005) A decision-theoretic approach to task assistance for persons with dementia. In: IJCAI international joint conference on artificial intelligenceGoogle Scholar
  2. 2.
    Bouchard B, Giroux S, Bouzouane A (2006) A smart home agent for plan recognition of cognitively-impaired patients. J Comput 1(5):53–62Google Scholar
  3. 3.
    Jennings NR, Wooldridge MJ (1998) Agent technology: foundations, applications and marketsGoogle Scholar
  4. 4.
    Tang Z, Guo J, Miao S, Acharya S, Feng JH (2016) Ambient intelligence based context-aware assistive system to improve independence for people with autism spectrum disorder. In: Proceedings of the annual Hawaii international conference on system sciences, pp 3339–3348Google Scholar
  5. 5.
    Meditskos G, Kompatsiaris I (2017) iKnow: ontology-driven situational awareness for the recognition of activities of daily living. Pervasive Mob Comput 40:17–41CrossRefGoogle Scholar
  6. 6.
    Zolfaghari S, Zall R, Keyvanpour MR (2016) SOnAr: Smart Ontology Activity recognition framework to fulfill Semantic Web in smart homes Samaneh. In: Proceedings of the annual hawaii international conference on system sciences, pp 3339–3348Google Scholar
  7. 7.
    Sycara K, Paolucci M, Lewis M (2003) Information discovery and fusion: semantics on the battlefield. In: Proceedings of the 6th international conference on information fusion, FUSION 2003Google Scholar
  8. 8.
    Laskey KB, Haberlin R, Costa P, Carvalho RN (2011) PR-OWL 2 case study: a maritime domain probabilistic ontology. CEUR Workshop Proceedings, vol 808, pp 76–83Google Scholar
  9. 9.
    Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Factors J Hum Factors Ergon Soc 37(1):32–64CrossRefGoogle Scholar
  10. 10.
    Noor MHM, Salcic Z, Wang KIK (2018) Ontology-based sensor fusion activity recognition. J Ambient Intell Humaniz Comput 1–15Google Scholar
  11. 11.
    W3C: The time ontology in OWL.
  12. 12.
    Stanford University, University, S.: ProtégéGoogle Scholar
  13. 13.
    Apache: Apache Jena.
  14. 14.
    Neo4j: Neo4j graph platform – the leader in graph databases.
  15. 15.
    Semantic Web: semantic Web/RDF liberary for C#.NET.
  16. 16.
    W3C: Euler proof mechanism.
  17. 17.
    Harris S, Gibbins N (2003) 3store: efficient bulk rdf storage. In: Proceedings of the 1st international workshop on practical and scalable semantic systems (PSSS’03)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and InformaticsDe Montfort UniversityLeicesterUK
  2. 2.School of ComputingUlster UniversityBelfastUK

Personalised recommendations