• DocumentCode
    2950255
  • Title

    TRUSS: Tracking Risk with Ubiquitous Smart Sensing

  • Author

    Mayton, Brian ; Dublon, G. ; Palacios, S. ; Paradiso, Joseph A.

  • Author_Institution
    Responsive Environments Group, MIT Media Lab., Cambridge, MA, USA
  • fYear
    2012
  • fDate
    28-31 Oct. 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    We present TRUSS, or Tracking Risk with Ubiquitous Smart Sensing, a novel system that infers and renders safety context on construction sites by fusing data from wearable devices, distributed sensing infrastructure, and video. Wearables stream real-time levels of dangerous gases, dust, noise, light quality, altitude, and motion to base stations that synchronize the mobile devices, monitor the environment, and capture video. At the same time, low-power video collection and processing nodes track the workers as they move through the view of the cameras, identifying the tracks using information from the sensors. These processes together connect the context-mining wearable sensors to the video; information derived from the sensor data is used to highlight salient elements in the video stream. The augmented stream in turn provides users with better understanding of real-time risks, and supports informed decision-making. We tested our system in an initial deployment on an active construction site.
  • Keywords
    computerised instrumentation; construction industry; data mining; safety systems; sensor fusion; sensors; video signal processing; TRUSS; altitude information; base stations; construction sites; context mining wearable sensor; dangerous gas; data fusion; distributed sensing infrastructure; dust information; light quality; motion information; noise information; risk tracking; ubiquitous smart sensing; video signal processing; wearable device; Base stations; Cameras; Safety; Sensor fusion; Streaming media; Tracking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Sensors, 2012 IEEE
  • Conference_Location
    Taipei
  • ISSN
    1930-0395
  • Print_ISBN
    978-1-4577-1766-6
  • Electronic_ISBN
    1930-0395
  • Type

    conf

  • DOI
    10.1109/ICSENS.2012.6411393
  • Filename
    6411393