Wearable Accelerometer Optimal Positions for Human Motion Recognition

Chengshuo Xia, Yuta Sugiura

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

Abstract

An intelligent human activity recognition system is influenced to some extent by sensor placement. In this paper, the number, and placement positions, of wearable accelerometers have been investigated to determine their influence on a human activity recognition system. Given 17 possible human sensor placements, we developed a multi-stage and multi-swarm discrete particle swarm optimization algorithm to explore the optimal sensor combination for various required sensor amounts. Relevant experimentation involved 10 different human daily activities, achieving an average prediction accuracy for a 4-sensor optimal combination of 95.12% via support vector machine classifier. The number and corresponding placement of sensors required for activity recognition have also been provided in this paper.

Original languageEnglish
Title of host publicationLifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages19-20
Number of pages2
ISBN (Electronic)9781728170633
DOIs
Publication statusPublished - 2020 Mar
Event2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020 - Kyoto, Japan
Duration: 2020 Mar 102020 Mar 12

Publication series

NameLifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies

Conference

Conference2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020
CountryJapan
CityKyoto
Period20/3/1020/3/12

Keywords

  • amount and position
  • motion recognition
  • particle swarm optimization
  • wearable accelerometer

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Xia, C., & Sugiura, Y. (2020). Wearable Accelerometer Optimal Positions for Human Motion Recognition. In LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (pp. 19-20). [9081172] (LifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LifeTech48969.2020.1570618961