Chapter

Neural Nets and Surroundings

Volume 19 of the series Smart Innovation, Systems and Technologies pp 41-50

An Experimental Evaluation of Reservoir Computation for Ambient Assisted Living

  • Davide BacciuAffiliated withDipartimento di Informatica, Università di Pisa Email author 
  • , Stefano ChessaAffiliated withDipartimento di Informatica, Università di Pisa
  • , Claudio GallicchioAffiliated withDipartimento di Informatica, Università di Pisa
  • , Alessio MicheliAffiliated withDipartimento di Informatica, Università di Pisa
  • , Paolo BarsocchiAffiliated withPisa Research Area, ISTI-CNR

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Abstract

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.

Keywords

Reservoir Computing Echo State Networks Wireless Sensor Networks Ambient Assisted Living