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채민기(Min-Gi Chae),정준영(Jun-Young Jung),박철제(Chul-Je Park),장인훈(In-Hun Jang),박현섭(Hyun Sub Park) 제어로봇시스템학회 2012 제어·로봇·시스템학회 논문지 Vol.18 No.7
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.
허원호(Won ho He),김은태(Euntai Kim),박현섭(Hyun Sub Park),정준영(Jun-Young Jung) 제어로봇시스템학회 2015 제어·로봇·시스템학회 논문지 Vol.21 No.6
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.