Applied Intelligence

, Volume 38, Issue 1, pp 88–98 | Cite as

EEM: evolutionary ensembles model for activity recognition in Smart Homes

  • Muhammad FahimEmail author
  • Iram Fatima
  • Sungyoung Lee
  • Young-Koo Lee


Activity recognition requires further research to enable a multitude of human-centric applications in the smart home environment. Currently, the major challenges in activity recognition include the domination of major activities over minor activities, their non-deterministic nature and the lack of availability of human-understandable output. In this paper, we introduce a novel Evolutionary Ensembles Model (EEM) that values both minor and major activities by processing each of them independently. It is based on a Genetic Algorithm (GA) to handle the non-deterministic nature of activities. Our evolutionary ensemble learner generates a human-understandable rule profile to ensure a certain level of confidence for performed activities. To evaluate the EEM, we performed experiments on three different real world datasets. Our experiments show significant improvement of 0.6 % to 0.28 % in the F-measures of recognized activities compared to existing counterparts. It is expected that EEM would be a practical solution for the activity recognition problem due to its understandable output and improved accuracy.


Activity recognition Evolutionary ensemble Genetic algorithm Smart Home 



This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0030823)


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Muhammad Fahim
    • 1
    Email author
  • Iram Fatima
    • 1
  • Sungyoung Lee
    • 1
  • Young-Koo Lee
    • 1
  1. 1.Ubiquitous Computing Lab, Department of Computer EngineeringKyung Hee UniversityYongin-siKorea

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