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Demo Abstract: Occupancy and activity monitoring with Doppler sensing and edge analytics

  • Yang Zhao
  • , Jeff Ashe
  • , David Toledano
  • , Brandon Good
  • , Li Zhang
  • , Adam McCann
  • General Electric

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We demonstrate an occupancy detection and human activ-ity monitoring system using low-cost motion sensing, edge computing and wireless networking devices. We design a dual Doppler sensor to achieve high signal sensitivity and wide sensing range. We develop signal filtering, detection and machine learning algorithms on an embedded computer to generate classification result in real-Time. We also imple-ment web services to enable users to access signal and room state via a wireless network. Compared with conventional occupancy sensors, the dual Doppler system has higher de-tection rate, and also has the capability of detecting activi-ties even for multiple people.

Original languageEnglish
Title of host publicationProceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
PublisherAssociation for Computing Machinery
Pages322-323
Number of pages2
ISBN (Electronic)9781450342636
DOIs
StatePublished - 14 Nov 2016
Externally publishedYes
Event14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016 - Stanford, United States
Duration: 14 Nov 201616 Nov 2016

Publication series

NameProceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016

Conference

Conference14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
Country/TerritoryUnited States
CityStanford
Period14/11/1616/11/16

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