Applied Intelligence

, Volume 48, Issue 8, pp 2017–2030 | Cite as

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

  • Zaineb LiouaneEmail author
  • Tayeb Lemlouma
  • Philippe Roose
  • Fréderic Weis
  • Hassani Messaoud


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.


ELM RELM Elderly Behavior prediction Smart home Accuracy Time series prediction 



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


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Zaineb Liouane
    • 1
    • 2
    Email author
  • Tayeb Lemlouma
    • 2
  • Philippe Roose
    • 3
  • Fréderic Weis
    • 2
  • Hassani Messaoud
    • 1
  1. 1.LARATSIMonastir UniversityMonastirTunisia
  2. 2.IRISARennes I UniversityRennesFrance
  3. 3.LIUPPA/T2iPau and the Adour Countries UniversityAngletFrance

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