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A Gait Phase Classifier using a Recurrent Neural Network

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  • Heo, Won ho (Electrical & Electronic Engineering, Yonsei University) ;
  • Kim, Euntai (Electrical & Electronic Engineering, Yonsei University) ;
  • Park, Hyun Sub (Human-Assistive Robot Research Center, Korea Institute of Industrial Technology) ;
  • Jung, Jun-Young (Human-Assistive Robot Research Center, Korea Institute of Industrial Technology)
  • 허원호 (연세대학교 전기전자공학부, 한국생산기술연구원 인간지원로봇연구단) ;
  • 김은태 (연세대학교 전기전자공학부) ;
  • 박현섭 (한국 산업기술평가관리원 로봇PD실) ;
  • 정준영 (한국생산기술연구원 인간지원로봇연구단)
  • Received : 2015.02.15
  • Accepted : 2015.03.15
  • Published : 2015.06.01

Abstract

This paper proposes a gait phase classifier using a Recurrent Neural Network (RNN). Walking is a type of dynamic system, and as such it seems that the classifier made by using a general feed forward neural network structure is not appropriate. It is known that an RNN is suitable to model a dynamic system. Because the proposed RNN is simple, we use a back propagation algorithm to train the weights of the network. The input data of the RNN is the lower body's joint angles and angular velocities which are acquired by using the lower limb exoskeleton robot, ROBIN-H1. The classifier categorizes a gait cycle as two phases, swing and stance. In the experiment for performance verification, we compared the proposed method and general feed forward neural network based method and showed that the proposed method is superior.

Acknowledgement

Supported by : 산업통상자원부

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