Experimental analysis of deep echo state networks for ambient assisted living

The Reservoir Computing (RC) paradigm represents a stateof- the-art methodology for efficient building of recurrent neural networks, which in the last years has proved effective in learning real-world temporal tasks from streams of sensorial data in the Ambient Assisted Living (AAL) domain. Recently, the study of RC networks has been extended to the case of deep architectures, with the introduction of the deep Echo State Network (DeepESN) model. Featured by a layered composition of recurrent units, DeepESNs are inherently able to develop a hierarchically structured representation of temporal information, at the same time preserving the RC characterization of training efficiency. In this paper, we discuss the introduction of the DeepESN approach in the field of AAL. To this aim, we perform a comparative experimental analysis on two real-world benchmark datasets related to inferring the user's behavior from data streams gathered from the nodes of a wireless sensor network. Results show that DeepESNs outperform standard RC networks with shallow architecture, suggesting a multiple-time scales nature of the involved temporal data and pointing out the great potentiality of the proposed approach in the AAL field.