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Robust EMG Pattern Recognition to Muscular Fatigue Effect for Human-Machine Interaction

  • Jae-Hoon Song
  • Jin-Woo Jung
  • Zeungnam Bien
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)

Abstract

The main goal of this paper is to design an electromyogram (EMG) pattern classifier which is robust to muscular fatigue effects for human-machine interaction. When a user operates some machines such as a PC or a powered wheelchair using EMG-based interface, muscular fatigue is generated by sustained duration time of muscle contraction. Therefore, recognition rates are degraded by the muscular fatigue. In this paper, an important observation is addressed: the variations of feature values due to muscular fatigue effects are consistent for sustained duration time. From this observation, a robust pattern classifier was designed through the adaptation process of hyperboxes of Fuzzy Min-Max Neural Network. As a result, significantly improved performance is confirmed.

Keywords

Contraction Time Central Fatigue Muscular Fatigue Prosthetic Hand Peripheral Fatigue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jae-Hoon Song
    • 1
  • Jin-Woo Jung
    • 2
  • Zeungnam Bien
    • 3
  1. 1.Air Navigation and Traffic System DepartmentKorea Aerospace Research InstituteDaejeonKorea
  2. 2.Department of Computer EngineeringDongguk UniversitySeoulKorea
  3. 3.Department of Electrical Engineering and Computer ScienceKorea Advanced Institute of Science and TechnologyDaejeonKorea

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