An Experimental Evaluation of Reservoir Computation for Ambient Assisted Living

  • Davide Bacciu
  • Stefano Chessa
  • Claudio Gallicchio
  • Alessio Micheli
  • Paolo Barsocchi
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 19)


In this paper we investigate the introduction of Reservoir Computing (RC) neural network models in the context of AAL (Ambient Assisted Living) and self-learning robot ecologies, with a focus on the computational constraints related to the implementation over a network of sensors. Specifically, we experimentally study the relationship between architectural parameters influencing the computational cost of the models and the performance on a task of user movements prediction from sensors signal streams. The RC shows favorable scaling properties results for the analyzed AAL task.


Reservoir Computing Echo State Networks Wireless Sensor Networks Ambient Assisted Living 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Davide Bacciu
    • 1
  • Stefano Chessa
    • 1
  • Claudio Gallicchio
    • 1
  • Alessio Micheli
    • 1
  • Paolo Barsocchi
    • 2
  1. 1.Dipartimento di InformaticaUniversità di PisaPisaItaly
  2. 2.Pisa Research AreaISTI-CNRPisaItaly

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