dc.contributor.author | Dublon, Gershon | |
dc.contributor.author | Paradiso, Joseph A. | |
dc.contributor.author | Mayton, Brian Dean | |
dc.contributor.author | Palacios, Sebastian Ricardo | |
dc.date.accessioned | 2013-09-11T21:04:06Z | |
dc.date.available | 2013-09-11T21:04:06Z | |
dc.date.issued | 2012-10 | |
dc.identifier.isbn | 978-1-4577-1767-3 | |
dc.identifier.isbn | 978-1-4577-1766-6 | |
dc.identifier.isbn | 978-1-4577-1765-9 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/80408 | |
dc.description.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. | en_US |
dc.description.sponsorship | Intel Corporation | en_US |
dc.description.sponsorship | Massachusetts Institute of Technology. Media Laboratory | en_US |
dc.description.sponsorship | Eni S.p.A. (Firm) | en_US |
dc.language.iso | en_US | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/ICSENS.2012.6411393 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike 3.0 | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/ | en_US |
dc.source | MIT Web Domain | en_US |
dc.title | TRUSS: Tracking Risk with Ubiquitous Smart Sensing | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Mayton, 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.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Media Laboratory | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Responsive Environments Group | en_US |
dc.contributor.mitauthor | Mayton, Brian Dean | en_US |
dc.contributor.mitauthor | Dublon, Gershon | en_US |
dc.contributor.mitauthor | Palacios, Sebastian Ricardo | en_US |
dc.contributor.mitauthor | Paradiso, Joseph A. | en_US |
dc.relation.journal | 2012 IEEE Sensors | en_US |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dspace.orderedauthors | Mayton, Brian; Dublon, Gershon; Palacios, Sebastian; Paradiso, Joseph A. | en_US |
dc.identifier.orcid | https://orcid.org/0000-0001-7524-1106 | |
dc.identifier.orcid | https://orcid.org/0000-0001-9195-1829 | |
dc.identifier.orcid | https://orcid.org/0000-0002-0719-7104 | |
dc.identifier.orcid | https://orcid.org/0000-0002-5421-8334 | |
mit.license | OPEN_ACCESS_POLICY | en_US |
mit.metadata.status | Complete | |