Cost-sensitive feature selection for on-body sensor localization

Ramyar Saeedi, Brian Schimert, Hassan Ghasemzadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

Abstract

Activity recognition systems have demonstrated potential in a broad range of applications. A crucial aspect of creating large scale human activity sensing corpus is to develop algorithms that perform activity recognition in a way that users are not limited to wear sensors on predefined locations on the body. Therefore, effective on-body sensor localization algorithms are needed to detect the location of wearable sensors automatically and in real-time. However, power optimization is a major concern in the design of these systems. Frequent need to charge multiple sensor nodes imposes much burden on the end-users. In this paper, we propose a novel signal processing approach that leverages feature selection algorithms to minimize power consumption of node localization. With the real data collected using wearable motion sensors, we demonstrate that the proposed approach achieves an energy saving that ranges from 88% to 99.59% while obtaining an accuracy performance between 73.15% and 99.85%.

Original languageEnglish (US)
Title of host publicationUbiComp 2014 - Adjunct Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PublisherAssociation for Computing Machinery, Inc
Pages833-842
Number of pages10
ISBN (Electronic)9781450330473
DOIs
StatePublished - 2014
Externally publishedYes
Event2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014 - Seattle, United States
Duration: Sep 13 2014Sep 17 2014

Publication series

NameUbiComp 2014 - Adjunct Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Conference

Conference2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014
Country/TerritoryUnited States
CitySeattle
Period9/13/149/17/14

Keywords

  • Body sensor networks (BSNs)
  • Classification
  • Low power design
  • Machine learning
  • On-body sensor localization

ASJC Scopus subject areas

  • Software

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