Motion based adaptive step length estimation using smartphone

Jung Ho Lee, Beomju Shin, Seok Lee, Jae Hun Kim, Chulki Kim, Taikjin Lee, Jinwoo Park

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

4 Citations (Scopus)

Abstract

This paper presents a motion recognition based step length estimation algorithm using smartphone. Motion of a user is identified based on the hybrid model of Decision Tree (DT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The parameters of linear combination based step length model are adapted based on the result motion recognition. In order to verify the proposed algorithm, we performed experiments on 5 subjects and showed accuracy of step length estimation as RMSE.

Original languageEnglish
Title of host publicationProceedings of the International Symposium on Consumer Electronics, ISCE
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479945924
DOIs
Publication statusPublished - 2014 Jan 1
Event18th IEEE International Symposium on Consumer Electronics, ISCE 2014 - Jeju, Korea, Republic of
Duration: 2014 Jun 222014 Jun 25

Other

Other18th IEEE International Symposium on Consumer Electronics, ISCE 2014
CountryKorea, Republic of
CityJeju
Period14/6/2214/6/25

ASJC Scopus subject areas

  • Engineering(all)

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    Lee, J. H., Shin, B., Lee, S., Kim, J. H., Kim, C., Lee, T., & Park, J. (2014). Motion based adaptive step length estimation using smartphone. In Proceedings of the International Symposium on Consumer Electronics, ISCE [6884456] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCE.2014.6884456