Consiglio Nazionale delle Ricerche

Tipo di prodottoContributo in atti di convegno
TitoloLeveraging RF signals for human sensing: fall detection and localization in human-machine shared workspaces,"
Anno di pubblicazione2015
FormatoElettronico
Autore/iS. Kianoush, S. Savazzi, F. Vicentini, V. Rampa, M. Giussani,
Affiliazioni autoriIEIIT-CNR, ITIA-CNR
Autori CNR e affiliazioni
  • SANAZ KIANOUSH
  • FEDERICO VICENTINI
  • STEFANO SAVAZZI
  • VITTORIO RAMPA
Lingua/e
  • inglese
AbstractSafe human-machine interactions promote high flexibility in collaborative workspaces. Fall detection and localization of the operator are major issues in ensuring a safe working environment. However, many proposed solutions are not applicable for deployment in industrial environments due to their performance limitations in practical contexts. In this paper, we propose an integrated framework for both localization and fall detection of operators inside a shared workspace that employs radiofrequency (RF) signal analysis in real-time. Multipath and nonline- of-sight (NLOS) scattering that affect RF signal propagation can be leveraged for human sensing in complex workspaces: the proposed system continuously monitors the fluctuations of the RF field across the space by a dense network of WiFi compliant radio devices operating at 2.4GHz. To increase the accuracy of the localization system, a sensor fusion algorithm using Extended Kalman Filter techniques is employed. The proposed method may be used for integrating measurements from both RF nodes and an additional image-based system. For fall detection, a Hidden Markov Model is applied to discern different postures of the operator and to detect a fall event by tracking the fluctuations of the wireless signal quality. Fall detector performances are validated through experimental measurements. The preliminary results confirm the effectiveness of the proposed approach in terms of sensitivity and specificity to correctly detect a fall event from pre-impact postures. Finally, some results about sensor fusion for improved operator localization are presented.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da1456
Pagine a1462
Pagine totali7
Rivista-
Numero volume della rivista-
Serie/Collana-
Titolo del volume-
Numero volume della serie/collana-
Curatore/i del volume-
ISBN-
DOI-
Editore-
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:116400)
Parole chiaveKalman filters, RSSI, feature extraction, hidden Markov models, human computer interaction, sensor fusion, wireless LAN, NLOS scattering, RF signal analysis, RF signal propagation, WiFi compliant radio device, collaborative workspace, extended Kalman filter technique, hidden Markov model, human-machine interaction, human-machine shared workspace, radio-frequency signal analysis
Link (URL, URI)http://ieeexplore.ieee.org/xpl/abstractAuthors.jsp?reload=true&arnumber=7281947
Titolo convegno/congressoIEEE 13th International Conference on Industrial Informatics (INDIN)
Luogo convegno/congressoCambridge, UK
Data/e convegno/congresso2015
RilevanzaInternazionale
RelazioneContributo
Titolo parallelo-
Note/Altre informazioni-
Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
  • ITIA — Istituto di tecnologie industriali e automazione
Moduli CNR
  • ICT.P08.006.001 : Tecnologie avanzate per l'interazione uomo, robot ed agenti intelligenti
  • ICT.P07.005.001 : Reti wireless integrate per accesso ad alta velocita'
Progetti Europei-
Allegati
Leveraging RF signals for human sensing: fall detection and localization in human-machine shared workspaces
Descrizione: In this paper, we propose an integrated framework for both localization and fall detection of operators inside a shared workspace that employs radiofrequency (RF) signal analysis in real-time. Multipath and nonline- of-sight (NLOS) scattering that affect RF signal propagation can be leveraged for human sensing in complex workspaces: the proposed system continuously monitors the fluctuations of the RF field across the space by a dense network of WiFi compliant radio devices operating at 2.4GHz. To increase the accuracy of the localization system, a sensor fusion algorithm using Extended Kalman Filter techniques is employed. The proposed method may be used for integrating measurements from both RF nodes and an additional image-based system.
Tipo documento: application/pdf