Skip to main content

Advertisement

SpringerLink
Log in

EEM: evolutionary ensembles model for activity recognition in Smart Homes

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

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.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Parisa RC, Diane JH, Lawrence B, Maureen S (2011) Discovering activities to recognize track in a smart environment. IEEE Trans Knowl Data Eng 23(4):527–539

    Article  Google Scholar 

  2. Munguia TE, Intille SS, Larson K (2004) Activity recognition in the home setting using simple and ubiquitous sensors, PERVASIVE

  3. Pavan T, Chellappa R, Subrahmanian VS, Udrea O (2008) Machine recognition of human activities: a survey. IEEE Trans Circuits Syst Video Technol 18(11):1473–1488

    Article  Google Scholar 

  4. Vinh LT, Lee S, Xuan HL, Hung QN, Hyoung K, Manhyung H, Lee YK (2011) Semi-Markov conditional random fields for accelerometer-based activity recognition. Appl Intell 35(2):226–241

    Article  Google Scholar 

  5. Kasteren T, Noulas A, Englebienne G, Krose B (2008) Accurate activity recognition in a home setting. In: Proceedings of the 10th international conference on ubiquitous computing, New York, 2008

    Google Scholar 

  6. Shi LD, Shi YH, Gao Y, Shan L, Bin Y (2011) XCSC: a nove approach to clustering with extended classifier system. Int J Neural Syst 21(1):79–93

    Article  Google Scholar 

  7. Bull L, Studley M, Bagnall A, Whittley I (2007) Learning classifier system ensembles with rule-sharing. IEEE Trans Evol Comput 11(4):496–502

    Article  Google Scholar 

  8. Kuncheva LI, Jain LC (2000) Designing classifier fusion systems by genetic algorithms. IEEE Trans Evol Comput 4(4):327–336

    Article  Google Scholar 

  9. Kim KJ, Cho SB (2008) An evolutionary algorithm approach to optimal ensemble classifiers for DNA microarray data analysis. IEEE Trans Evol Comput 12(3):377–388

    Article  Google Scholar 

  10. Folino G, Pizzuti C, Spezzano G (2010) An ensemble-based evolutionary framework for coping with distributed intrusion detection. Genet Program Evol Mach 11(2):131–146

    Article  Google Scholar 

  11. Cuntoor NP, Yegnanarayana B, Chellappa R (2008) Activity modeling using event probability sequences. IEEE Trans Image Process 17(4):594–607

    Article  MathSciNet  Google Scholar 

  12. Liao L, Choudhury T, Fox D, Kautz H (2007) Training conditional random fields using virtual evidence boosting. In: Proceedings of the International Joint Conference on Artificial Intelligence, Hyederabad, 2007

  13. Chen L, Nugent CD, Wang H (2011) A knowledge-driven approach to activity recognition in smart homes. IEEE Trans Knowl Data Eng 99:1

    Google Scholar 

  14. Zhu C, Cheng Q, Sheng W (2010) Human activity recognition via motion and vision data fusion. In: IEEE asilomar conference on signals, systems, and computers, Pacific Grove, 2010

    Google Scholar 

  15. Mitchell T (1997) Machine learning. McGraw Hill, Columbus

    MATH  Google Scholar 

  16. Puig AO, Mansilla EB (2008) Evolutionary rule-based systems for imbalanced data sets. Soft Comput 13(3):213–225

    Article  Google Scholar 

  17. van Kasteren TLM, Alemdar H, Ersoy C (2011) Effective performance metrics for evaluating. In: Second workshop on context-systems design, evaluation and optimisation, Italy, 2011

    Google Scholar 

  18. Jehad AMS, Lee YK, Lee S (2010) A smoothed naive Bayes-based classifier for activity recognition. IETE Tech Rev 27(2):107–119

    Article  Google Scholar 

  19. Parisa R, Cook DJ (2010) Mining and monitoring patterns of daily routines for assisted living in real world. In: Proceedings of the 1st ACM international health informatics symposium, New York, 2010

    Google Scholar 

  20. Popescu M, Florea E (2008) Linking clinical events in elderly to in-home monitoring sensor data: a brief review and a pilot study on predicting pulse pressure. J Comput Inf Sci Eng 2(2):180–199

    Google Scholar 

  21. Xing H, Qu R (2011) A compact genetic algorithm for the network coding based resource minimization problem. Appl Intell 36(4):809–823

    Article  Google Scholar 

  22. Shin KS, Jeong YS, Jeong MK (2011) A two-leveled symbiotic evolutionary algorithm for clustering problems. Appl Intell 36(4):788–799

    Article  Google Scholar 

Download references

Acknowledgement

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

Author information

Authors and Affiliations

  1. Ubiquitous Computing Lab, Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do, 446-701, Korea

    Muhammad Fahim, Iram Fatima, Sungyoung Lee & Young-Koo Lee

Authors
  1. Muhammad Fahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iram Fatima
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sungyoung Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Young-Koo Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Fahim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fahim, M., Fatima, I., Lee, S. et al. EEM: evolutionary ensembles model for activity recognition in Smart Homes. Appl Intell 38, 88–98 (2013). https://doi.org/10.1007/s10489-012-0359-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-012-0359-7

Keywords

Search

Navigation