http://www.cnr.it/ontology/cnr/individuo/prodotto/ID277112
An experimental characterization of reservoir computing in ambient assisted living applications (Articolo in rivista)
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- Label
- An experimental characterization of reservoir computing in ambient assisted living applications (Articolo in rivista) (literal)
- Anno
- 2014-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1007/s00521-013-1364-4 (literal)
- Alternative label
Bacciu D., Barsocchi P., Chessa S., Gallicchio C., Micheli A. (2014)
An experimental characterization of reservoir computing in ambient assisted living applications
in Neural computing & applications (Print)
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Bacciu D., Barsocchi P., Chessa S., Gallicchio C., Micheli A. (literal)
- Pagina inizio
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
- [Online First 16 March 2013]
ISSN 0941-0643 (print) 1433-3058 (online)
codice originale cnr.isti/2012-TR-002
Grant agreement: 269914
Tipo Progetto: EU_FP7 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://link.springer.com/article/10.1007%2Fs00521-013-1364-4 (literal)
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- ISI Web of Science (WOS) (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Computer Science Department, University of Pisa, Italy; CNR-ISTI, Pisa, Italy; Computer Science Department, University of Pisa, Italy; Computer Science Department, University of Pisa, Italy; Computer Science Department, University of Pisa, Italy (literal)
- Titolo
- An experimental characterization of reservoir computing in ambient assisted living applications (literal)
- Abstract
- 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. (literal)
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