Skip to main content
SpringerLink
Log in

An improved extreme learning machine model for the prediction of human scenarios in smart homes

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

One of the main objectives of smart homes is healthcare monitoring and assistance, especially for elderly and disabled people. Therefore, an accurate prediction of the inhabitant behavior is very helpful to provide the required assistance. This work aims to propose a prediction model that satisfies the accuracy as well as the rapidity of the learning phase. To do so, we propose to improve the existing extreme learning machine (ELM) model by defining a recurrent form. This form ensures a temporal relationship of inputs between observations at different time steps. The new model uses feedback connections to the input layer from the output layer which allows the output to be included in the long-term prediction. A recurrent dynamic network, with feedback connections of the output of the network, is proposed to predict the future series representing future activities of the inhabitant. The resulting model, called Recurrent Extreme Learning Machine (RELM), provides the ability to learn the human behavior and ensures a good balance between the learning time and the prediction accuracy. The input data is based on the real data representing the activities of persons belonging to the profile of first level (i.e. P 1) as measured by the dependency model called Functional Autonomy Measurement System (SMAF) used in the geriatric domain. The experimental results reveal that the proposed RELM model requires a minimum time during the learning phase with a better performance compared to existing models.

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

Similar content being viewed by others

References

  1. Bazi Y, Alajlan N, Melgani F (2014) Differential evolution extreme learning machine for the classification of hyperspectral images. IEEE Geosci Remote Sens Lett 11(6):1066–1070

    Article  Google Scholar 

  2. Barata J, Carlos A, Hussein M (2012) The Moore–Penrose Pseudoinverse: A tutorial review of the theory. Braz J Phys 42(1-2):146–165

    Article  Google Scholar 

  3. Bin L, Yibin L, Xuewen R (2011) Comparison of echo state network and extreme learning machine on nonlinear prediction. J Comput Inf Syst 7(6):1863–1870

    Google Scholar 

  4. Rong Y, Zhang G, Chang Y, Huang Y (2016) Integrated optimization model of laser brazing by extreme learning machine and genetic algorithm. Int J Adv Manuf Technol 87(9-12):2943–2950

    Article  Google Scholar 

  5. Huang G, Zhu Q, Siew C (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501

    Article  Google Scholar 

  6. Janakiraman VM, Nguyen X, Assanis D (2016) Stochastic gradient based extreme learning machines for stable online learning of advanced combustion engines. Neurocomputing 177:304–316

    Article  Google Scholar 

  7. Qu Y, Shen Q, Parthaláin N (2010) Extreme learning machine for mammographic risk analysis. In: UK workshop computational intelligence (UKCI). IEEE, Piscataway, pp 1–5

  8. Sridevi N, Subashini P (2013) Combining Zernike moments with regional features for classification of handwritten ancient tamil scripts using extreme learning machine. emerging trends in computing communication and nanotechnology (ICE-CCN). In: International conference on IEEE, pp 158–162

  9. Wang D, Wang R, Yan H (2014) Fast prediction of protein–protein interaction sites based on extreme learning machines. Neurocomputing 128:258–266

    Article  Google Scholar 

  10. Yage Z, Zhihua C, Wenyin G, Xinxin W (2015) Self-adaptive differential evolution extreme learning machine and its application in water quality evaluation. J Comput Inf Syst 11(4):1443–1451

    Google Scholar 

  11. Ortín S, Soriano MC, Pesquera L, Brunner D, San-Martín D, Fischer I, Gutiérrez JM (2015) A unified framework for reservoir computing and extreme learning machines based on a single time-delayed neuron. Sci Report 5:14945

    Article  Google Scholar 

  12. Goudarzi A, Banda P, Lakin M (2014) A Comparative Study of Reservoir Computing for Temporal Signal Processing. Tech. Rep. University of New Mexico

  13. Wang GG, Lu M, Dong YQ, Zhao XJ (2016) Self-adaptive extreme learning machine. Neural Comput & Applic 27(2):291–303

    Article  Google Scholar 

  14. Huang G, Huang GB, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48

    Article  MATH  Google Scholar 

  15. Mahmoud S, Lotfi A, Langensiepen C (2013) Behavioral pattern identification and prediction in intelligent environments. Appl Soft Comput 13(4):1813–1822

    Article  Google Scholar 

  16. Undesa (2012) Population division united nations department of economic and social affairs

  17. Skouby KE, Kivimäki A, Haukiputo L (2014) Smart cities and the ageing population. The 32nd Meeting of WWRF

  18. Zaineb L, Tayeb L, Philippe R, Fréderic W, Hassani M (2016) A markovian-based approach for daily living activities recognition. In: International conference on sensor networks (SENSORNETS’16)

  19. Zheng H, Wang H, Black N (2008) Human activity detection in smart home environment with self-adaptive neural networks. In: IEEE international conference on networking, sensing and control, 2008. ICNSC, vol 2008, pp 1505–1510

  20. Oniga S, Suto J (2014) Human activity recognition using neural networks. In: 15th international carpathian control conference (ICCC). IEEE, Piscataway , pp 403–406

  21. Oniga S, Suto J (2016) Activity recognition in adaptive assistive systems using artificial neural networks. Elektronika ir Elektrotechnika 22(1):68–72

    Article  Google Scholar 

  22. Mshali HH, Lemlouma T, Magoni D (2015) Context-aware Adaptive Framework for e-Health Monitoring. In: IEEE international conference on data science and data intensive system, pp 276–283

  23. Guo T, Xu Z, Yao X, Chen H, Aberer K, Funaya K (2016) Robust online time series prediction with recurrent neural networks. In: IEEE international conference on data science and advanced analytics, pp 816–825

  24. Prasad SC, Prasad P (2014) Deep recurrent neural networks for time series prediction. arXiv:1407.5949

  25. Çatak FÖ (2015) Classification with boosting of extreme learning machine over arbitrarily partitioned data. Soft Comput 21(9):2269–2281

    Article  Google Scholar 

  26. Ren X, Ding W, Crouter SE, Mu Y, Xie R (2016) Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning. Appl Intell 45(2):512–529

    Article  Google Scholar 

  27. Zheng H, Wang H, Black N (2008) Human activity detection in smart home environment with self-adaptive neural networks. Networking, Sensing and Control 9:1505–1510

    Google Scholar 

  28. Liu Z, Song Y, Shang Y, Wang J (2015) Posture recognition algorithm for the elderly based on BP neural networks. In: Control and decision conference, pp 1446–1449

  29. Hussein A, Adda M, Atieh M (2014) Smart home design for disabled people based on neural networks. Procedia Computer Science 37:117–126

    Article  Google Scholar 

  30. Teich T, Roessler F, Kretz D et al (2014) Design of a prototype neural network for smart homes and energy efficiency. Procedia Engineering 69:603–608

    Article  Google Scholar 

  31. Uddin MZ, Lee JJ, Kim TS (2010) Independent shape component-based human activity recognition via Hidden Markov Model. Appl Intell 33(2):193–206

    Article  Google Scholar 

  32. Fang H, He L (2012) BP Neural network for human activity recognition in smart home. In: International conference on computer science and service system, pp 1034–1037

  33. Graf C (2009) The lawton instrumental activities of daily living (IADL) scale. MEDSURG Nursing: Official journal of the Academy of Medical-Surgical Nurses 18(5):315–316

    Google Scholar 

  34. Fahim M, Fatima I, Lee S, Lee Y (2013) EEM: Evolutionary ensembles model for activity recognition in Smart Homes. Appl Intell 38(1):88–98

    Article  Google Scholar 

  35. Hébert R, Raîche M, Dubois MF, Gueye NR, Tousignant M (2012) Development of indicators to promote measures for the prevention and rehabilitation of functional decline in older people. Revue d’epidemiologie et de sante publique 60(6):463–472

    Article  Google Scholar 

  36. Liouane Z, Lemlouma T, Roose P, Weis F, Messaoud H (2016) An improved elman neural network for daily living activities recognition. In: International conference on intelligent systems design and applications. Springer, Cham, pp 697–707

  37. Lotfi A, Langensiepen C, Mahmoud SM, Akhlaghinia MJ (2012) Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour. J Ambient Intell Humaniz Comput 3(3):205–218

    Article  Google Scholar 

  38. Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175

    Article  MATH  Google Scholar 

  39. Patra A, Das S, Mishra SN, Senapati MR (2017) An adaptive local linear optimized radial basis functional neural network model for financial time series prediction. Neural Comput Applic 28(1):101–110

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to express their thanks to all the team of the project “e-Health Monitoring Open Data project”.

Author information

Authors and Affiliations

  1. LARATSI, Monastir University, Ibn El Jazzar, Monastir, Tunisia

    Zaineb Liouane & Hassani Messaoud

  2. IRISA, Rennes I University, Lannion, Rennes, France

    Zaineb Liouane, Tayeb Lemlouma & Fréderic Weis

  3. LIUPPA/T2i, Pau and the Adour Countries University, Anglet, France

    Philippe Roose

Authors
  1. Zaineb Liouane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tayeb Lemlouma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Philippe Roose
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fréderic Weis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hassani Messaoud
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zaineb Liouane.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liouane, Z., Lemlouma, T., Roose, P. et al. An improved extreme learning machine model for the prediction of human scenarios in smart homes. Appl Intell 48, 2017–2030 (2018). https://doi.org/10.1007/s10489-017-1062-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-017-1062-5

Keywords

Search

Navigation