A gait phase classifier using a recurrent neural network

Won Ho Heo, Euntai Kim, Hyun Sub Park, Jun Young Jung

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)518-523
Number of pages6
JournalJournal of Institute of Control, Robotics and Systems
Issue number6
Publication statusPublished - 2015 Jan 1

Bibliographical note

Publisher Copyright:
© ICROS 2015.

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Applied Mathematics


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