Article

Robotics is gaining its popularity in gait rehabilitation. Gait pattern planning
is important to ensure that the gait patterns induced by robotic systems are
tailored to each individual and varying walking speed. Most research groups
planned gait patterns for their robotics systems based on Clinical Gait Analysis
(CGA) data. The major problem with the method using the CGA data is that it
cannot accommodate inter-subject differences. In addition, CGA data is limited to
only one walking speed as per the published data. The objective of this work was
to develop an individual-specific gait pattern prediction model for gait pattern
planning in the robotic gait rehabilitation systems. The waveforms of lower limb
joint angles in the sagittal plane during walking were obtained with a motion
capture system. Each waveform was represented and reconstructed by a Fourier
coefficient vector which consisted of eleven elements. Generalized regression
neural networks (GRNNs) were designed to predict Fourier coefficient vectors from
given gait parameters and lower limb anthropometric data. The generated waveforms
from the predicted Fourier coefficient vectors were compared to the actual
waveforms and CGA waveforms by using the assessment parameters of correlation
coefficients, mean absolute deviation (MAD) and threshold absolute deviation
(TAD). The results showed that lower limb joint angle waveforms generated by the
gait pattern prediction model were closer to the actual waveforms compared to the
CGA waveforms.
CI - Copyright (c) 2013 Elsevier B.V. All rights reserved.

Langue : ANGLAIS