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dc.contributor.authorDublon, Gershon
dc.contributor.authorParadiso, Joseph A.
dc.contributor.authorMayton, Brian Dean
dc.contributor.authorPalacios, Sebastian Ricardo
dc.date.accessioned2013-09-11T21:04:06Z
dc.date.available2013-09-11T21:04:06Z
dc.date.issued2012-10
dc.identifier.isbn978-1-4577-1767-3
dc.identifier.isbn978-1-4577-1766-6
dc.identifier.isbn978-1-4577-1765-9
dc.identifier.urihttp://hdl.handle.net/1721.1/80408
dc.description.abstractWe 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.en_US
dc.description.sponsorshipIntel Corporationen_US
dc.description.sponsorshipMassachusetts Institute of Technology. Media Laboratoryen_US
dc.description.sponsorshipEni S.p.A. (Firm)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICSENS.2012.6411393en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceMIT Web Domainen_US
dc.titleTRUSS: Tracking Risk with Ubiquitous Smart Sensingen_US
dc.typeArticleen_US
dc.identifier.citationMayton, Brian, Gershon Dublon, Sebastian Palacios, and Joseph A. Paradiso. “TRUSS: Tracking Risk with Ubiquitous Smart Sensing.” In 2012 IEEE Sensors, 1-4. Institute of Electrical and Electronics Engineers, 2012.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Responsive Environments Groupen_US
dc.contributor.mitauthorMayton, Brian Deanen_US
dc.contributor.mitauthorDublon, Gershonen_US
dc.contributor.mitauthorPalacios, Sebastian Ricardoen_US
dc.contributor.mitauthorParadiso, Joseph A.en_US
dc.relation.journal2012 IEEE Sensorsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsMayton, Brian; Dublon, Gershon; Palacios, Sebastian; Paradiso, Joseph A.en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-7524-1106
dc.identifier.orcidhttps://orcid.org/0000-0001-9195-1829
dc.identifier.orcidhttps://orcid.org/0000-0002-0719-7104
dc.identifier.orcidhttps://orcid.org/0000-0002-5421-8334
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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