Predicting user movements in heterogeneous indoor environments by reservoir computing (Rapporti tecnici/preprint/working paper)

Type

• Prodotto della ricerca (Classe)
• Rapporti tecnici/preprint/working paper (Classe)

Label

• Predicting user movements in heterogeneous indoor environments by reservoir computing (Rapporti tecnici/preprint/working paper) (literal)

Anno

• 2011-01-01T00:00:00+01:00 (literal)

Alternative label

• Bacciu D., Barsocchi P., Chessa S., Gallicchio C., Micheli A. (2011)
  Predicting user movements in heterogeneous indoor environments by reservoir computing
  
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Bacciu D., Barsocchi P., Chessa S., Gallicchio C., Micheli A. (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni

• Grant agreement 269914 - Tipo Progetto EU_FP7 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

• 6 (literal)

Note

• uMa (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#supporto

• Altro (literal)

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

• Predicting user movements in heterogeneous indoor environments by reservoir computing (literal)

Abstract

• Anticipating user localization by making accurate predictions on its indoor movement patterns is a fundamental challenge for achieving higher degrees of personalization and reactivity in smarthome environments. We propose an approach to real-time movement forecasting founding on the efficient Reservoir Computing paradigm, predicting user movements based on streams of Received Signal Strengths collected by wireless motes distributed in the home environment. The ability of the system to generalize its predictive performance to unseen ambient configurations is experimentally assessed in challenging conditions, comprising external test scenarios collected in home environments that are not included in the training set. Experimental results suggest that the system can effectively generalize acquired knowledge to novel smart-home setups, thereby delivering an higher level of personalization while decreasing costs for installation and setup. (literal)

Prodotto di

• Tecnologie e sistemi wireless eterogenei interconnessi (ICT.P07.008.002) (Modulo)
• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)

Autore CNR

• STEFANO CHESSA (Persona)
• PAOLO BARSOCCHI (Persona)

Insieme di parole chiave

• Parole chiave di "Predicting user movements in heterogeneous indoor environments by reservoir computing" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)
• Tecnologie e sistemi wireless eterogenei interconnessi (ICT.P07.008.002) (Modulo)

Autore CNR di

• STEFANO CHESSA (Persona)
• PAOLO BARSOCCHI (Persona)

Insieme di parole chiave di

• Parole chiave di "Predicting user movements in heterogeneous indoor environments by reservoir computing" (Insieme di parole chiave)