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An experimental characterization of reservoir computing in ambient assisted living applications


In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system configurations toward the embedding into computationally constrained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world applications. First, the approach is assessed on a validation scheme where training, validation and test data are sampled in homogeneous ambient conditions, i.e., from the same set of rooms. Then, it is introduced an external test set involving a new setting, i.e., a novel ambient, which was not available in the first phase of model training and validation. The specific test-bed considered in the paper allows us to investigate the capability of the RC approach to discriminate among user movement trajectories from received signal strength indicator sensor signals. This capability can be exploited in various AAL applications targeted at learning user indoor habits, such as in the proposed indoor movement forecasting task. Such a joint analysis of the efficiency/efficacy trade-off provides novel insight in the concrete successful exploitation of RC for AAL tasks and for their distributed implementation into wireless sensor networks.

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  1. This paper, based on ex-novo conducted experiments, extends the preliminary investigations presented in the conference papers [18], proposing a comprehensive vision of the costs that can practically occur for real deployment of the RC models into mote devices. In particular, previous works did not include an exhaustive comparative analysis of different network sizes and configurations [4, 18], or the adoption of a reduced reservoir weight-encoding scheme [4, 18] or the external performance evaluation [18].

  2. Here, we refer to the general usage of the leaking parameter a in the RC literature. Note, however, that Eq. 1 represents a reduced form of the complete leaky integrator dynamics [22, 24, 30, 41]. In this regard, the characterization of Eq. 1 is that of a low-pass filter with smoothing parameter a.

  3. Crossbow Technology Inc.,

  4. Note that for this task, learning has the general aim of distinguishing among the different types of user trajectories (straight or curved in our scenario), based only on the history of noisy RSS signals. On the other hand, the specific application presented in this paper, consisting in the prediction of the user localization (room change or not), represents a concrete example of real-life RC application in the field of AAL.


  6. Note that the choice of the connectivity, leaking rate and spectral radius values is not critical for the task. The experiments in this section use the same values identified in previous preliminary works, e.g., Gallicchio et al. [18].

  7. Note that the results reported for the non-local 2 and non-local 3 settings are averaged over all the possible configurations of the available anchors (i.e., 6 configurations for non-local 2 and 4 configurations for non-local 3).



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This work is partially supported by the EU FP7 RUBICON project (, Contract no. 269914.

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Correspondence to Claudio Gallicchio.

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Bacciu, D., Barsocchi, P., Chessa, S. et al. An experimental characterization of reservoir computing in ambient assisted living applications. Neural Comput & Applic 24, 1451–1464 (2014).

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  • Ambient assisted living
  • Reservoir computing
  • Wireless sensor networks
  • Indoor user movement forecasting