Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloDevice-Free RF Human Body Fall Detection and Localization in Industrial Workplaces
Anno di pubblicazione2017
  • Elettronico
  • Cartaceo
Autore/iSanaz Kianoush ; Stefano Savazzi ; Federico Vicentini ; Vittorio Rampa ; Matteo Giussani
Affiliazioni autoriS. Kianoush, IEIIT-CNR, Milan, Italy, S. Savazzi, IEIIT-CNR, Milan, Italy, V. Rampa , IEIIT-CNR, Milan, Italy, F. Vicentini, ITIA-CNR, Milan, Italy, M. Giussani,ITIA-CNR, Milan, Italy.
Autori CNR e affiliazioni
  • inglese
AbstractFall detection and localization of human operators inside a workspace are major issues in ensuring a safe working environment. Recent research has shown that the perturbations of the radio-frequency (RF) signals commonly adopted for wireless communications can also be used as sensing tools for device-free human motion detection. Device-free RF-based human sensing applications range from tag-less body localization to detection and monitoring of human well-being (e-Health). In this paper, we propose a real-time system for human body motion sensing with special focus on joint body localization and fall detection. The proposed system continuously monitors and processes the RF signals emitted by industry-compliant radio devices operating in the 2.4 GHz ISM band and supporting machine-to-machine (M2M) communication functions. Human-induced diffraction and multipath phenomena that affect RF signal propagation are leveraged for body localization while for fall detection a Hidden Markov Model is applied to discern different postures of the operator and to detect safety-relevant events by tracking the received signal strength indicator footprints. Fall detection performances are corroborated by extensive experimental measurements in different settings. In addition, we propose also a sensor fusion tool that is able to integrate the device-free RF-based sensing system within an industrial image sensors framework. Preliminary results, conducted during field trial measurements, confirm the effectiveness of the proposed approach in terms of localization accuracy, and sensitivity/specificity to correctly detect a fall event from pre-impact postures.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da351
Pagine a362
Pagine totali12
RivistaIEEE Internet of Things Journal
Attiva dal 2014
Editore: Institute of Electrical and Electronics Engineers - New York, NY
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 2327-4662
Titolo chiave: IEEE Internet of Things Journal
Numero volume della rivista4
Fascicolo della rivista2
Verificato da refereeSì: Internazionale
Stato della pubblicazionePreprint
Indicizzazione (in banche dati controllate)-
Parole chiaveDevice free localization, device free activity recognition, human machine workplace
Link (URL, URI)
Titolo parallelo-
Data di accettazione03/11/2016
Note/Altre informazioniPubblicato in formato elettronico il 3 Novembre 2016; pubblicato su rivista durante il mese di Aprile 2017
Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
  • STIIMA — Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato
Moduli CNR
    Progetti Europei-
    • Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces
      Descrizione: This is the accepted version of the IEEE-copyrighted article "Device-Free RF Human Body Fall Detection and Localization in Industrial Workplaces'" already available online at the link