EEM: Evolutionary ensembles model for activity recognition in Smart Homes

Muhammad Fahim*, Iram Fatima, Sungyoung Lee, Young Koo Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)88-98
Number of pages11
JournalApplied Intelligence
Issue number1
Publication statusPublished - Jan 2013
Externally publishedYes

Bibliographical note

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

Copyright 2013 Elsevier B.V., All rights reserved.


  • Activity recognition
  • Evolutionary ensemble
  • Genetic algorithm
  • Smart Home
  • Machine Learning

ASJC Scopus subject areas

  • Artificial Intelligence


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