Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloCellular data analytics for detection and discrimination of body movements
Anno di pubblicazione2018
Formato-
Autore/iSTEFANO SAVAZZI, SANAZ KIANOUSH, VITTORIO RAMPA, UMBERTO SPAGNOLINI
Affiliazioni autoriricercatore, assegnista post dottorato, ricercatore,professore
Autori CNR e affiliazioni
  • SANAZ KIANOUSH
  • STEFANO SAVAZZI
  • VITTORIO RAMPA
Lingua/e
  • inglese
AbstractIn this paper, we show the possibility of using the smartphone built-in cellular radio modem to track sudden changes in the environment around it, thus turning the cellphone into a radio-frequency (RF) virtual sensor. In particular, we demonstrate how to isolate anomalous RF patterns by applying time series modelling and analysis of downlink multi-cell radio signals. These RF anomalies may indicate a situation change, namely a body, or object(s), movement in the surrounding of the smart-phone. Unlike WiFi and Bluetooth devices, that can be turned on and off according to the user demands, cellular radios are never really disconnected. Even in idle mode, they carry out continuous and autonomous measurements of the radio channel conditions, namely the cellular signal quality (CSQ). This is performed in agreement with standardized cell reselection procedures. Body movements, or scene changes in general, in the surroundings of a cellular device are responsible of small CSQ fluctuations that can be isolated from normal network operations and classified accordingly. Validation of this unconventional RF sensing method is based on extensive measurement campaigns covering a period of one month, using up to 4 commercial off-the-shelf smartphones. As a practical application case study, we developed a real-time demonstrator that is able to detect body proximity events close to the device and discriminate other bodyinduced environmental changes in the surrounding of the smartphone. Usage of data analytics tools for passive sensing from cellular signals is a novel topic that shows great potential as paving the way to new applications and research opportunities.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da-
Pagine a-
Pagine totali-
RivistaIEEE access
Attiva dal 2013
Editore: Institute of Electrical and Electronics Engineers - Piscataway, NJ
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 2169-3536
Titolo chiave: IEEE access
Titolo proprio: IEEE access
Numero volume della rivista-
Fascicolo della rivista-
DOI10.1109/ACCESS.2018.2869702
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiaveMotion detection, Wireless Wide Area Networking, Cellular Signal Quality, Anomaly Detection, Bayesian Classification, Segmentation, Data Analytics, Mobile Phone-Sensing, Machine Learning
Link (URL, URI)https://ieeexplore.ieee.org/document/8462756/
Titolo parallelo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
Moduli CNR-
Progetti Europei-
Allegati
  • Cellular data analytics for detection and discrimination of body movements
    Descrizione: In this paper, we show the possibility of using the smartphone built-in cellular radio modem to track sudden changes in the environment around it, thus turning the cellphone into a radio-frequency (RF) virtual sensor