An efficient sensing approach using dynamic multi-sensor collaboration for activity recognition

Lei Gao, Alan K. Bourke, John Nelson

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

Abstract

This paper presents an efficient sensing approach for activity recognition using multi-sensor fusion. The main achievement of the approach is to accurately recognize the human activity with the minimum body sensor usage through the use of dynamic sensor collaboration. The Nave Bayes Classifier is adopted as the classification engine due to not only its easy implementation but also the advantages for multi-sensor fusion. The sensor selection is based on the real-time assignment information value of each sensor node. The platform is composed of a base station and a number of sensor nodes. The base station is used to assign the real-time information value for each sensor node, and fuse the chosen sensor data.

Original languageEnglish
Title of host publication2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11
DOIs
Publication statusPublished - 2011
Event7th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS'11 - Barcelona, Spain
Duration: 27 Jun 201129 Jun 2011

Publication series

Name2011 International Conference on Distributed Computing in Sensor Systems and Workshops, DCOSS'11

Conference

Conference7th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS'11
Country/TerritorySpain
CityBarcelona
Period27/06/1129/06/11

Keywords

  • activity recognition
  • body sensor networks
  • dynamic sensor collaboration
  • multi-sensor fusion

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