Linked adaptive neuro-fuzzy inference system for biosignal distortion detection system

Jun Yong Park, Dong W. Kim, Tae Koo Kang, Myo Taeg Lim

Research output: Contribution to journalArticle

Abstract

This paper proposes a biosignal distortion detection algorithm for a driver healthcare system based on a contact biosensor and a linked adaptive neuro-fuzzy inference system (ANFIS), and demonstrate its superiority using actual vehicle experiments. Contact biosensors are highly sensitive to vehicle vibration and turning. Although vehicle suspension contributes significantly to ride quality, vibration transfers to the driver and contact between the driver and biosensor can become unstable when executing a turn, causing the driver's biosignal to not be measured well. This study estimated the driver's biosignal state using acceleration, angular velocity, and slip ratio measurements obtained from sensor fusion. When the measurement exceeded a defined threshold, the driver healthcare system removed unreliable biosignal data. We adopted ANFIS to improve the proposed sensor fusion algorithm estimate accuracy for the driver's biosignal state and improved the healthcare system robustness to road conditions. The effectiveness of the proposed algorithm was demonstrated experimentally by comparing the system using sensor fusion and linked ANFIS.

Original languageEnglish
Pages (from-to)7725-7735
Number of pages11
JournalJournal of Intelligent and Fuzzy Systems
Volume37
Issue number6
DOIs
Publication statusPublished - 2019 Jan 1

Keywords

  • biosignal distortion detection
  • driver healthcare system
  • Linked adaptive neuro fuzzy inference system
  • sensor fusion

ASJC Scopus subject areas

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence

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