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A multifunctional wireless body area sensors network with real time embedded data analysis

  • School of Electrical Engineering and Automation, Harbin Institute of Technology
  • Florida International University
  • School of Transportation Science and Engineering, Harbin Institute of Technology

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

Abstract

Quantitative limitation of sensor nodes, single function and limited data processing capability, are the major problems of traditional Wireless Body Area Network (WBAN). In many applications, numbers of sensor nodes used for multi-parameter synchronous measurements are necessary, which usually require real-time analysis and high portability. This paper presents a multifunctional WBAN with real time embedded data analysis ability. Each sensor node in the WBAN is capable of EEG/ECG/EOG, respiration impedance and body posture measurement. The DSP-embedded node, namely the main node, is solely responsible for the on-line data processing. The experiments demonstrated that this platform is able to afford more than 16 nodes simultaneously, which maintains a low data loss rate and high signal quality even for high sampling rate applications. With the advantage of DSP core, this platform performs perfectly in processing complex data acquired by multiple sensor nodes in real time.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages508-511
Number of pages4
ISBN (Electronic)9781509029594
DOIs
StatePublished - 2016
Externally publishedYes
Event12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016 - Shanghai, China
Duration: 17 Oct 201619 Oct 2016

Publication series

NameProceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016

Conference

Conference12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
Country/TerritoryChina
CityShanghai
Period17/10/1619/10/16

Keywords

  • WBAN
  • embedded data analysis
  • multiple physiological parameters
  • real-time

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