Abstract
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, log in via an institution to check access.
Similar content being viewed by others
Notes
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.
Information entropy indicates a measure of disorder or randomness of information in a dataset.
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.
References
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
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
Chiu SL (1997) An efficient method for extracting fuzzy classification rules from high dimensional data. J Adv Comput Intell 1:31–36
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
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
Zadeh LA (1978) Fuzzy Sets as a basis for a theory of possibility. Fuzzy Sets Syst 1:3–28
Author information
Authors and Affiliations
Corresponding author
Rights 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). https://doi.org/10.1007/s12652-012-0156-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-012-0156-5