A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton RobotsReport as inadecuate




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1

Robot Group, Korea Institute of Industrial Technology, 143 Hanggaul-ro, Sanrok-gu, Ansan-si, Gyeonggi-do 15588, Korea

2

School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea

3

School of Intelligent Robots, University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea

4

System Industry PD Group, Korea Evaluation Institute of Industrial Technology, 32 Cheomdan-ro 8-gil, Dong-gu, Daegu 41069, Korea





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Author to whom correspondence should be addressed.



Academic Editor: Ning Xi

Abstract An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods a multilayer perceptron and nonlinear autoregressive with external inputs NARX are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases. View Full-Text

Keywords: exoskeleton robots; gait phase classification; neural network; MLP; NARX exoskeleton robots; gait phase classification; neural network; MLP; NARX





Author: Jun-Young Jung 1,* , Wonho Heo 1,2, Hyundae Yang 1,3 and Hyunsub Park 1,4

Source: http://mdpi.com/



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