Construction of predictive models for bicycle riding comfort evaluation using electromyogram and electroencephalogram

Noriki Toyoshima, Suguru Kanoga, Yasue Mitsukura

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

1 Citation (Scopus)

Abstract

In the bicycle manufacturing industries, manufacturers attempt to reflect a user's preference, namely riding comfort, on their products. Surface electromyogram (EMG)-based approaches have been researched for evaluation of riding comfort. However, the EMG does not capture user preferences, because it focuses on muscle fatigue, not riding comfort. To solve this problem, we propose an approach that combines an electroencephalogram (EEG) generated from the brain, which controls modulation of feelings and thoughts. Two bicycles that have different parameter settings and two types of tracks (straight and slalom) were selected to determine the riding comfort, especially riding difference, for the first time by using an EMG and EEG. Elastic net logistic regression analysis was used to construct predictive models. The classification accuracy of the bicycles was determined to be 81.9±7.0% for the slalom course. Furthermore, it was demonstrated that the rectus muscle and frontal lobe are important points for evaluation of the riding comfort of bicycles.

Original languageEnglish
Title of host publicationProceeding - 2016 IEEE 12th International Colloquium on Signal Processing and its Applications, CSPA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages100-104
Number of pages5
ISBN (Electronic)9781467387804
DOIs
Publication statusPublished - 2016 Jul 18
Event12th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2016 - Melaka, Malaysia
Duration: 2016 Mar 42016 Mar 6

Other

Other12th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2016
CountryMalaysia
CityMelaka
Period16/3/416/3/6

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Keywords

  • Bicycle
  • electroencephalogram (EEG)
  • electromyogram (EMG)
  • riding comfort

ASJC Scopus subject areas

  • Signal Processing
  • Control and Systems Engineering
  • Control and Optimization

Cite this

Toyoshima, N., Kanoga, S., & Mitsukura, Y. (2016). Construction of predictive models for bicycle riding comfort evaluation using electromyogram and electroencephalogram. In Proceeding - 2016 IEEE 12th International Colloquium on Signal Processing and its Applications, CSPA 2016 (pp. 100-104). [7515812] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSPA.2016.7515812