A Gait Phase Classifier using a Recurrent Neural Network

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

doi:10.5302/j.icros.2015.15.9024

Cited by 5 publications

(1 citation statement)

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“…Next, five phases of the gait were identified by the C4.5 decision tree algorithm. Heo [27] used recurrent neural networks (RNN) to categorize a gait cycle into a swing phase and a stance phase. The input data of the RNN was composed of joint angles alongside its velocities and a back propagation algorithm was used to train the network.…”

Section: Related Workmentioning

confidence: 99%

Gait Phase Classification and Assist Torque Prediction for a Lower Limb Exoskeleton System Using Kernel Recursive Least-Squares Method

Wang

et al. 2019

Sensors

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The gait phase classification method is a key technique to control an exoskeleton robot. Different people have different gait features while wearing an exoskeleton robot due to the gap between the exoskeleton and the wearer and their operation habits, such as the correspondence between the joint angle and the moment at which the foot contacts the ground, the amplitude of the joint angle and others. In order to enhance the performance of the gait phase classification in an exoskeleton robot using only the angle of hip and knee joints, a kernel recursive least-squares (KRLS) algorithm is introduced to build a gait phase classification model. We also build an assist torque predictor based on the KRLS algorithm in this work considering the adaptation of unique gait features. In this paper, we evaluate the classification performance of the KRLS model by comparing with two other commonly used gait recognition methods—the multi-layer perceptron neural network (MLPNN) method and the support vector machine (SVM) algorithm. In this experiment, the training and testing datasets for the models built by KRLS, MLPNN and SVM were collected from 10 healthy volunteers. The gait data are collected from the exoskeleton robot that we designed rather than collected from the human body. These data depict the human-robot coupling gait that includes unique gait features. The KRLS classification results are in average 3% higher than MLPNN and SVM. The testing average accuracy of KRLS is about 86%. The prediction results of KRLS are twice as good as MLPNN in assist torque prediction experiments. The KRLS performs in a good, stable, and robust way and shows model generalization abilities.

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Section: Related Workmentioning

confidence: 99%