Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloA learning system for automatic Berg Balance Scale score estimation
Anno di pubblicazione2017
Formato
  • Elettronico
  • Cartaceo
Autore/iBacciu D.; Chessa S.; Gallicchio C.; Micheli A.; Pedrelli L.; Ferro E.; Fortunati L.; La Rosa D.; Palumbo F.; Vozzi F.; Parodi O.
Affiliazioni autoriComputer Science Department, University of Pisa, Pisa, Italy; Computer Science Department, University of Pisa, Pisa, Italy; Computer Science Department, University of Pisa, Pisa, Italy; Computer Science Department, University of Pisa, Pisa, Italy; Computer Science Department, University of Pisa, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-IFC, Pisa, Italy; CNR-IFC, Pisa, Italy
Autori CNR e affiliazioni
  • STEFANO CHESSA
  • FILIPPO PALUMBO
  • LUIGI FORTUNATI
  • ERINA FERRO
  • FEDERICO VOZZI
  • OBERDAN PARODI
  • DAVIDE LA ROSA
Lingua/e
  • inglese
AbstractThe objective of this work is the development of a learning system for the automatic assessment of balance abilities in elderly people. The system is based on estimating the Berg Balance Scale (BBS) score from the stream of sensor data gathered by a Wii Balance Board. The scientific challenge tackled by our investigation is to assess the feasibility of exploiting the richness of the temporal signals gathered by the balance board for inferring the complete BBS score based on data from a single BBS exercise. The relation between the data collected by the balance board and the BBS score is inferred by neural networks for temporal data, modeled in particular as Echo State Networks within the Reservoir Computing (RC) paradigm, as a result of a comprehensive comparison among different learning models. The proposed system results to be able to estimate the complete BBS score directly from temporal data on exercise #10 of the BBS test, with ?10 s of duration. Experimental results on real-world data show an absolute error below 4 BBS score points (i.e. below the 7% of the whole BBS range), resulting in a favorable trade-off between predictive performance and user's required time with respect to previous works in literature. Results achieved by RC models compare well also with respect to different related learning models. Overall, the proposed system puts forward as an effective tool for an accurate automated assessment of balance abilities in the elderly and it is characterized by being unobtrusive, easy to use and suitable for autonomous usage.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da60
Pagine a74
Pagine totali-
RivistaEngineering applications of artificial intelligence
Attiva dal 1988
Editore: Pineridge, - Swansea
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 0952-1976
Titolo chiave: Engineering applications of artificial intelligence
Titolo proprio: Engineering applications of artificial intelligence.
Titolo abbreviato: Eng. appl. artif. intell.
Numero volume della rivista66
Fascicolo della rivista-
DOI10.1016/j.engappai.2017.08.018
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85029795329)
  • ISI Web of Science (WOS) (Codice:000413798600006)
Parole chiaveBalance assessment, Berg Balance Scale, Echo State Network, Learning with temporal data, Reservoir computing
Link (URL, URI)http://www.sciencedirect.com/science/article/pii/S0952197617302026
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IFC — Istituto di fisiologia clinica
  • ISTI — Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
Moduli/Attività/Sottoprogetti CNR
  • ICT.P07.008.002 : Tecnologie e sistemi wireless eterogenei interconnessi
Progetti Europei
Allegati
A learning system for automatic Berg Balance Scale score estimation (documento privato )
Descrizione: Codice PuMa: 2017-A0-044
Tipo documento: application/pdf
postprint
Descrizione: postprint version
Tipo documento: application/pdf