Skip to main content
SpringerLink
Log in

Learning patterns in ambient intelligence environments: a survey

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

It is essential for environments that aim at helping people in their daily life that they have some sort of Ambient Intelligence. Learning the preferences and habits of users then becomes an important step in allowing a system to provide such personalized services. Thus far, the exploration of these issues by the scientific community has not been extensive, but interest in the area is growing. Ambient Intelligence environments have special characteristics that have to be taken into account during the learning process. We identify these characteristics and use them to highlight the strengths and weaknesses of developments so far, providing direction to encourage further development in this specific area of Ambient Intelligence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Aarts E (2004) Ambient intelligence: a multimedia perspective. IEEE Multimed 11(1): 12–19

    Article  Google Scholar 

  • Akyldiz IF, Su W, Sankarasubramanian Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun Mag 40: 102–114

    Article  Google Scholar 

  • Allen J (1984) Towards a general theory of action and time. In: Artificial Intelligence, vol. 23. pp 123–154

  • Augusto J (2007) Ambient intelligence: the confluence of pervasive computing and artificial intelligence. In: Schuster A (eds) Intelligent computing everywhere. Springer, Berlin, pp 213–234

    Chapter  Google Scholar 

  • Augusto J, Cook D (2007) Ambient Intelligence: applications in society and opportunities for AI. In: Tutorial lecture notes delivered at 20th international joint conference on artificial intelligence (IJCAI-07). IJCAI, Hyderabad, India

  • Augusto JC, Nugent CD (2004) The use of temporal reasoning and management of complex events in smart homes. In: Proccedings of European conference on AI (ECAI 2004). IO Press, pp 778–782

  • Augusto JC, Nugent CD (2006) Smart homes can be smarter. In: Augusto JC, Nugent CD (eds) Designing smart homes. The role of artificial intelligence. Springer, Berlin, pp 1–15

    Chapter  Google Scholar 

  • Aztiria A, Augusto JC, Izaguirre A (2008a) Spatial and temporal aspects for pattern representation and discovery in intelligent environments. In: Workshop on spatial and temporal reasoning at 18th European conference on artificial intelligence (ECAI 2008) (to be published)

  • Aztiria A, Augusto JC, Izaguirre A, Cook DJ (2008b) Learning accurate temporal relations from user actions in intelligent environments. In: Proceedings of the 3rd symposium of ubiquitous computing and ambient intelligence, vol. 51/2009. pp 274–283

  • Begg R, Hassan R (2006) Artificial neural networks in smart homes. In: Augusto JC, Nugent CD (eds) Designing smart homes. The role of artificial intelligence. Springer, Berlin, pp 146–164

    Chapter  Google Scholar 

  • Berger JO (1985) Statistical decisions. Springer, Berlin

    Google Scholar 

  • Boisvert A, Rubio RB (1999) Architecture for intelligent thermostats that learn from occupants’ behavior. In: ASHRAE transactions. pp 124–130

  • Brammer K, Siffling G (1989) Kalman bucy filters. Springer, Artech House

  • Brdiczka O, Reignier P, Crowley JL (2005) Supervised learning of an abstract context model for an intelligent environment. In: Proceedings of the 2005 joint conference on smart objects and ambient intelligence: innovative context-aware services: usages and technologies, vol. 121. ACM, pp 259–264

  • Brdiczka O, Reignier P, Crowley J (2007) Detecting individual activities from video in a smart home. In: Proceedings of the international conference on knowledge-based and intelligent information and engineering systems. pp 363–370

  • Brezeal C, Scassellati B (1999) A context-dependent attention system for a social robot. In: Proceedings of the international joint conference on artificial intelligence. pp 1146–1151

  • Campo E, Bonhomme S, Chan M, Esteve D (2006) Learning life habits and practices: an issue to the smart home. In: international conference on smart Homes and health telematic. pp 355–358

  • Chan M, Hariton C, Ringeard P, Campo E (1995) Smart house automation system for the elderly and the disabled. In: Proceedings of the 1995 IEEE international conference on systems, man and cybernetics. pp 1586–1589

  • Coen MH (1998) Design principles for intelligent environments. In: Proceedings of the 1998 15th national conference on artificial intelligence, AAAI. AAAI Press, pp 547–554

  • Cook D, Augusto J, Jakkula V (2009) Ambient intelligence: applications in society and opportunities for ai. Pervasive Mob Comput 5(4): 277–298

    Article  Google Scholar 

  • Cook DJ, Das SK (2005) Smart environments: technology, protocols and applications. Wiley, New York

    Google Scholar 

  • Cook DJ, Das SK (2007) How smart are our environments? an updated look at the state of the art. In: Pervasive and mobile computing, Elsevier science, vol. 3. pp 53–73

  • Cook DJ, Huber M, Gopalratnam K, Youngblood M (2003) Learning to control a smart home environment. In: Innovative applications of artificial intelligence

  • Das SK, Cook DJ (2006) Designing and modeling smart environments. In: Proceedings of international sysmposium on wireless, mobile and multimedia networks. pp 490–494

  • Delapierre G, Grange H, Chambaz B, Destannes L (1983) Polymer-based capacitive humidity sensor. Sens Actuators 4(1): 97–104

    Google Scholar 

  • Doctor F, Hagras H, Callaghan V (2005) A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments. In: IEEE transactions on systems, man and cybernetics: part A, vol. 35. pp 55–65

  • Dooley J, Callaghan V, Hagras H, Bull P, Rohlfing D (2006) Ambient intelligence—knowledge representation, processing and distribution in intelligent inhabited environments. In: 2nd IET international conference on intelligent environments, IE 06, pp 51–59

  • Ducatel K, Bogdanowicz M, Scapolo F, Leijten J, Burgelman J (2001) Scenarios for ambient intelligence in 2010. Technical report URL http://cordis.europa.eu/ist/istagreports.htm

  • Duman H, Hagras H, Callaghan V (2008) Intelligent association exploration and exploitation of fuzzy agents in ambient intelligent environments. J Uncertain Syst 2(2): 133–143

    Google Scholar 

  • Ermes M, Parkka J, Mantyjarvi J, Korhonen I (2008) Detection of daily activities and sports with wearable sensors in controlled and uncontrolled contitions. IEEE Trans Inf Technol Biomed 12: 20–26

    Article  Google Scholar 

  • Friedemann M, Mahmoud N (2002) Pervasive computing, first international conference. Springer

  • Friedwald M, Costa OMD (2003) Science and technology roadmapping: ambient intelligence in everyday life (ami@life)

  • Friedwald M, Costa OMD, Punie Y, Alahuhta P, Heinonen S (2005) Perspectives of ambient intelligence in the home environment. In: Telematics and informatics, vol. 22. Pergamon Press, pp 221–238

  • Gal CL, Martin J, Lux A, Crowley JL (2001) Smartoffice: design of an intelligent environment. IEEE Intell Syst 16(4): 60–66

    Article  Google Scholar 

  • Galushka M, Patterson D, Rooney N (2006) Temporal data mining for smart homes. In: Augusto JC, Nugent CD (eds) Designing smart homes. The role of artificial intelligence. Springer, Berlin, pp 85–108

    Chapter  Google Scholar 

  • Gottfried B, Guesgen HW, Hubner S (2006) Spatiotemporal reasoning for smart homes. In: Designing smart homes. The role of artificial intelligence, m1: copyright 2006, the institution of engineering and technology. Springer, pp 16–34

  • Hagras H, Callaghan V, Colley M, Clarke G, Pounds-Cornish A, Duman H (2004) Creating an ambient-intelligence environment using embedded agents. IEEE Intell Syst 19(6): 12–20

    Article  Google Scholar 

  • Heierman EO, Cook DJ (2002) Improving home automation by discovering regularly occurring device usage patterns. In: Third IEEE international conference on data mining. pp 537–540

  • Jakkula VR, Crandall AS, Cook DJ (2007) Knowledge discovery in entity based smart environment resident data using temporal relation based data mining. In: 7th IEEE international conference on datamining. pp 625–630

  • Jiang L, Liu DY, Yang B (2004) Smart home research. In: Proceedings of 2004 international conference on machine learning and cybernetics, vol. 2. pp 659–663

  • Kautz H, Fox D, Etzioni O, Borriello G, Arnstein L (2002) An overview of the assisted cognition project. In: Proceedings of the AAAI workshop on automation as Caregiver. AAAI Press, pp 60–65

  • Kofod-Petersen A (2006) Challenges in case-based reasoning for context awareness in ambient intelligence systems. In: 1st workshop on case-based reasoning and context awareness

  • Kushwaha N, Kim M, Kim DY, Cho W (2004) An intelligent agent for ubiquitous computing environments: smart home ut-agent. In: Proceedings of the 2nd IEEE workshop on software technologies for future embedded and ubiquitous systems. pp 157–159

  • Leake D, Maguitman A, Reichherzer T (2006) Cases, context, and comfort: opportunities for case-based reasoning in smart homes. In: Augusto JC, Nugent CD (eds) Designing smart homes. The role of artificial intelligence. Springer, Berlin, pp 109–131

    Chapter  Google Scholar 

  • Liao L, Patterson DJ, Fox D, Kautz H (2004) Behavior recognition in assisted cognition. In: Proceedings of the IAAA-04 workshop on supervisory control of learning and adaptive systems. pp 41–42

  • Manyika J, Durrant-Whyte H (1994) Data fusion and sensor management: a decentralized information-theoretic approach. Ellis Horwood, Chichester

    Google Scholar 

  • Maurer U, Smailagic A, Siewiorek D, Deisher M (2006) Activity recognition and monitoring using multiple sensors on different body positions. In: Proceedings of the international workshop on wearable and implantable body sensor networks. pp 99–102

  • Mitchell TM (1997) Machine learning. McGraw-Hill/MIT Press

  • Mozer MC (2004) Lessons from an adaptive home. Smart environments: technology, protocols and applications. Wiley, pp 273–298

  • Mozer MC, Dodier RH, Anderson M, Vidmar L, Cruickshank RF, Miller D (1995) The neural network house: an overview. Erlbaum, pp 371–380. Current trends in connectionism

  • Muehlenbrock M, Brdiczka O, Snowdon D, Meunier J (2004) Learning to detect user activity and availability from a variety of sensor data. In: Proceedings of the IEEE international conference on pervasive computing and communications

  • Muller ME (2004) Can user models be learned at all? Inherent problems in machine learning for user modelling. In: Knowledge engineering review, vol. 19. Cambridge University Press, pp 61–88

  • Pantic M, Pentland A, Nijholt A, Huang T (2006) Human computing and machine understanding of human behavior: a survey. In: Proceedings of the 8th international conference on multimodal interfaces. ACM, pp 239–248

  • Partala T, Surakka V, Vanhala T (2006) Real-time estimation of emotional experiences from facial expressions. Interact Comput 18(2): 208–226

    Article  Google Scholar 

  • Pineau J, Montemerlo M, Pollack M, Roy N, Thrun S (2003) Towards robotic assistants in nursing homes: challenges and results. Rob Auton Syst 42(3–4): 271–281

    Article  MATH  Google Scholar 

  • Pollack ME (2005) Intelligent technology for an aging population: the use of ai to assist elders with cognitive impairment. AI Magazine 26(2): 9–24

    Google Scholar 

  • Ramos C, Augusto J, Shapiro D (2008) Ambient intelligence—the next step for artificial intelligence (guest editors’ introduction to the special issue on ambient intelligence). IEEE Intell Syst 23(2): 15–18

    Article  Google Scholar 

  • Rao SP, Cook DJ (2004) Predicting inhabitant action using action and task models with application to smart homes. Int J Artif Intell Tools (Architectures, Languages, Algorithms) 13(1): 81–99

    Article  Google Scholar 

  • Rivera-Illingworth F, Callaghan V, Hagras H (2005) A neural network agent based approach to activity detection in ami environments. In: IEEE international workshop on intelligent environments. pp 92–99

  • Rutishauser U, Joller J, Douglas R (2005) Control and learning of ambience by an intelligent building. In: IEEE on systems, man and cybernetics: a special issue on ambient intelligence, IEEE systems, man, and cybernetics society. pp 121–132

  • Sadeh NM, Gandom FL, Kwon OB (2005) Ambient intelligence: the mycampus experience. Technical report CMU-ISRI-05-123, ISRI

  • Schneider M, Krner A, Alvarado JCE, Higuera AG, Augusto JC, Cook DJ, Ikonen V, Cech P, Mikuleck P, Kameas A, Callaghan V (2009) 1st international workshop on rfid technology: concepts, practices and solutions. included in workshops proceedings of the 5th international conference on intelligent environments

  • Shadbolt N (2003) Ambient intelligence. IEEE Intell Syst 18(4): 2–3

    Article  Google Scholar 

  • Simpson R, Schreckenghost D, LoPresti EF, Kirsch N (2006) Plans and planning in smart homes. In: Augusto J, Nugent C (eds) Designing smart homes: the role of artificial intelligence. Springer, Berlin

    Google Scholar 

  • Stanford V (2004) Biosignals offer potential for direct interfaces and health monitoring. IEEE Pervasive Comput 3: 99–103

    Article  Google Scholar 

  • Stankovski V, Tmkoczy J (2006) Application of decision trees to smart homes. In: Augusto JC, Nugent CD (ed) Designing Smart Homes. The role of artificial intelligence, m1: copyright 2006, the institution of engineering and technology. Springer, pp 132–45

  • Tapia EM, Intille SS, Larson K (2004) Activity recognition in the home using simple and ubiquitous sensors. In: Proceedings of pervasive. pp 158–175

  • Turunen M, Hakulinen J, Kainulainen A, Melto A, Hurtig T (2007) Design of a rich multimodal interface for mobile spoken route guidance. In: Proceedings of interspeech 2007—Eurospeech

  • Vainio AM, Valtonen M, Vanhala J (2008) Proactive fuzzy control and adaptation methods for smart homes. IEEE Intell Syst 23(2): 42–49

    Article  Google Scholar 

  • Weiser M (1991) The computer for the 21st century. Sci Am 265(3): 94–104

    Article  Google Scholar 

  • Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Elsevier, Amsterdam

    MATH  Google Scholar 

  • Wolffenbuttel RF, Mahmoud KM, Regtien PL (1990) Compliant capacitive wrist sensor for use in industrial robots. IEEE Trans Instrum Meas 39: 991–997

    Article  Google Scholar 

  • Zaidenberg S, Reignier P, Crowley JL (2008) Reinforcement learning of context models for a ubiquitous personal assistant. In: Proceedings of the 3rd symposium of ubiquitous computing and ambient intelligence, vol. 51/2009. pp 254–264

Download references

Author information

Authors and Affiliations

  1. University of Mondragon, Mondragon, Spain

    Asier Aztiria & Alberto Izaguirre

  2. University of Ulster, Jordanstown, UK

    Juan Carlos Augusto

Authors
  1. Asier Aztiria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alberto Izaguirre
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juan Carlos Augusto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Asier Aztiria.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Aztiria, A., Izaguirre, A. & Augusto, J.C. Learning patterns in ambient intelligence environments: a survey. Artif Intell Rev 34, 35–51 (2010). https://doi.org/10.1007/s10462-010-9160-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-010-9160-3

Keywords

Search

Navigation