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

Noriki Toyoshima, Suguru Kanoga, Yasue Mitsukura

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceeding - 2016 IEEE 12th International Colloquium on Signal Processing and its Applications, CSPA 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ページ100-104
ページ数5
ISBN(電子版)9781467387804
DOI
出版ステータスPublished - 2016 7月 18
イベント12th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2016 - Melaka, Malaysia
継続期間: 2016 3月 42016 3月 6

出版物シリーズ

名前Proceeding - 2016 IEEE 12th International Colloquium on Signal Processing and its Applications, CSPA 2016

Other

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

ASJC Scopus subject areas

  • 信号処理
  • 制御およびシステム工学
  • 制御と最適化

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