Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Gait Phases Classification using Joint angle and Ground Reaction Force: Application of Backpropagation Neural Networks

관절각과 지면반발력을 이용한 보행 단계의 분류: 역전파 신경망 적용

  • 채민기 (과학기술연합대학원대학교 지능형로봇공학) ;
  • 정준영 (과학기술연합대학원대학교 지능형로봇공학) ;
  • 박철제 (과학기술연합대학원대학교 지능형로봇공학) ;
  • 장인훈 (한국생산기술연구원 로봇융합연구그룹) ;
  • 박현섭 (한국생산기술연구원 로봇융합연구그룹)
  • Received : 2012.04.30
  • Accepted : 2012.06.20
  • Published : 2012.07.01
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Abstract

This paper proposes the gait phase classifier using backpropagation neural networks method which uses the angle of lower body's joints and ground reaction force as input signals. The classification of a gait phase is useful to understand the gait characteristics of pathologic gait and to control the gait rehabilitation systems. The classifier categorizes a gait cycle as 7 phases which are commonly used to classify the sub-phases of the gait in the literature. We verify the efficiency of the proposed method through experiments.

Keywords

References

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