2014
Journal article  Open Access

An experimental characterization of reservoir computing in ambient assisted living applications

Bacciu D., Barsocchi P., Chessa S., Gallicchio C., Micheli A.

Wireless sensor networks  C.2.2 Network Protocols  Reservoir computing  Artificial Intelligence  Ambient Assisted Living  Software  Indoor user movement forecasting 

In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applica- tions. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system config- urations toward the embedding into computationally con- strained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world appli- cations. 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 vali- dation. 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 roposed 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.

Source: Neural computing & applications (Print) 24 (2014): 1451–1464. doi:10.1007/s00521-013-1364-4

Publisher: Springer., Godalming, Regno Unito


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BibTeX entry
@article{oai:it.cnr:prodotti:277112,
	title = {An experimental characterization of reservoir computing in ambient assisted living applications},
	author = {Bacciu D. and Barsocchi P. and Chessa S. and Gallicchio C. and Micheli A.},
	publisher = {Springer., Godalming, Regno Unito},
	doi = {10.1007/s00521-013-1364-4},
	journal = {Neural computing \& applications (Print)},
	volume = {24},
	pages = {1451–1464},
	year = {2014}
}

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