4.6 Article

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

Journal

SENSORS
Volume 15, Issue 11, Pages 27738-27759

Publisher

MDPI
DOI: 10.3390/s151127738

Keywords

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

Funding

  1. Korea Institute of Industrial Technology as Development of Human Coexistence Robot Platform and Smart Process Technology for Manufacturing Process Innovation [kitech EO-15-0022]
  2. National Research Council of Science & Technology (NST), Republic of Korea [EO150022] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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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.

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