Skip to main content

Activity modeling under uncertainty by trace of objects in smart homes


A typical resident of a smart home can be an Alzheimer patient that forgets sometimes to complete the activities that he begins. The key point to assist the smart home resident is to model the activities and discover correct realization patterns of activities. To accomplish this task, we apply sensors to provide primary data about realization patterns of actions, operations, plans, goals and generally any objective that the smart home resident may desire to do. In the consequence, by applying fuzzy clustering techniques, we are able to mine sensor data to retrieve the realization patterns of activities, and so the prediction patterns of intentions are recognizable. Comparing the realization patterns with prediction patterns of activities, we would be able to predict the intention of the resident about the activity that the resident considers to realize. In this way, we would be able to provide hypotheses about the resident goals and his possible goal achievement’s defects. Spatiotemporal aspects of daily activities such as movement of objects are surveyed to discover the patterns of activities realized by the smart homes residents. In this research, uncertainty is considered as a property of activity recognition.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7



  2. In LIARA more than 100 sensors (variable) are embedded in the Smart Home, and considering RFID tags, more than 700 features of activities are observed.

  3. Information entropy indicates a measure of disorder or randomness of information in a dataset.

  4. In the proposed case study, it is the location of RFID antennas that observe RFID tags in the environment. Their position is fixed in the environment and it can be said that they have absolute positions in the environment.




  • Acampora G, Gaeta M, Loia V, Vasilakos AV (2010) Interoperable and adaptive fuzzy services for ambient intelligence applications. ACM Transact Auton Adapt Syst 5(2):1–26

    Article  Google Scholar 

  • Amirjavid F, Bouzouane A, Bouchard B (2011a) Spatiotemporal knowledge representation and reasoning under uncertainty for action recognition in smart homes. In: Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference (MAICS’11), Cincinnati, Ohio, USA, 16–17 April, pp 188–194

  • Amirjavid F, Bouzouane A, Bouchard B (2011b) Proceedings of the International Conference on Conceptual Structures for Discovering Knowledge (ICCS 2011), 25–29 July. Lecture Notes in Artificial Intelligence (LNAI), Springer publisher, Derby, pp 353–357

  • Biswas J (2011) Perspectives in elder care with technology. In: Proceedings of the 9th Int. Conference on smart homes and Health Telematics (ICOST’11), 20–23 June. Lecture Notes in Computer Science (LNCS), Springer publisher, Montréal, pp 308–312

  • Bouchard B, Bouzouane A, Giroux S (2007) A keyhole plan recognition model for Alzheimer’s patients: first results, J Appl Artif Intell (AAI), Taylor & Francis publisher, vol 22(7): 623–658

    Google Scholar 

  • Chiu SL (1997) An efficient method for extracting fuzzy classification rules from high dimensional data. J Adv Comput Intell 1:31–36

    MathSciNet  Google Scholar 

  • Cook DJ, Youngblood M, Heierman EO, Gopalratnam K, Rao S, Litvin A, Khawaja F (2003) MavHome an agent-based smart home. In: Proceedings of the First IEEE International Conference on Pervasive Computing and Communications (PERCOM’03). IEEE Computer Society, Washington DC, 23–26 March, pp 521–524

  • Diamond J (2007) A report on Alzheimer disease and current research. Technical report. Alzheimer society of Canada

  • Gray R (2011) Entropy and information theory, 2nd edn. Springer publisher, Berlin, p 409. ISBN:978-1-4419-7969-8

  • Jakkula V, Cook DJ (2007) Temporal pattern discovery for anomaly detection in a smart home. In: Proceeding of the 3rd IET International Conference on Intelligent Environments (IE’07), pp 339–345

  • Nazerfard E, Rashidi P, Cook DJ (2010) Discovering temporal features and relations of activity patterns. In: Proceedings of the IEEE international conference on data mining workshops, pp 1069–1075

  • Priyono A, Ridwan M (2005) Generation of fuzzy rules with subtractive clustering. Jurnal Teknologi 43:143–153

    Article  Google Scholar 

  • Quinlan JR, Ghosh J (2006) Top 10 algorithms in data mining. In: Proceedings of the IEEE International Conference on Data Mining (ICDM’06). 18–22 Dec, Hong Kong

  • Roy P, Bouchard B, Bouzouane A, Giroux S (2010) A possibilistic approach for activity recognition in smart homes for cognitive assistance to Alzheimer’s patient. In: Chen L, Nugent C, Biswas J, Hoey J (eds), Activity recognition in pervasive intelligent environment (Atlantis ambient and pervasive intelligence), World Scientific Publishing Company, pp 31–56. ISBN:978-9078677352

  • Singla G, Cook DJ, Schmitter-Edgecomb M (2008) Incorporating temporal reasoning into activity recognition for smart home residents. In: Proceedings of the AAAI Workshop on Spatial and Temporal Reasoning, Chicago, Illinois, 13–17 July, pp 53–61

  • Zadeh LA (1968) Probability measures of fuzzy events. J Math Anal Appl 23:421–427

    Article  MATH  MathSciNet  Google Scholar 

  • Zadeh LA (1978) Fuzzy Sets as a basis for a theory of possibility. Fuzzy Sets Syst 1:3–28

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Farzad Amirjavid.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Amirjavid, F., Bouzouane, A. & Bouchard, B. Activity modeling under uncertainty by trace of objects in smart homes. J Ambient Intell Human Comput 5, 159–167 (2014).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Ambient environment
  • Fuzzy logic
  • Fuzzy subtractive clustering
  • Activity recognition
  • Temporal data mining