Ardestani, MM, Zhang, X, Wang, L et al. (5 more authors) (2014) Human lower extremity joint moment prediction: A wavelet neural network approach. Expert Systems with Applications, 41 (9). pp. 4422-4433. ISSN 0957-4174
Abstract
Joint moment is one of the most important factors in human gait analysis. It can be calculated using multi body dynamics but might not be straight forward. This study had two main purposes; firstly, to develop a generic multi-dimensional wavelet neural network (WNN) as a real-time surrogate model to calculate lower extremity joint moments and compare with those determined by multi body dynamics approach, secondly, to compare the calculation accuracy of WNN with feed forward artificial neural network (FFANN) as a traditional intelligent predictive structure in biomechanics. To aim these purposes, data of four patients walked with three different conditions were obtained from the literature. A total of 10 inputs including eight electromyography (EMG) signals and two ground reaction force (GRF) components were determined as the most informative inputs for the WNN based on the mutual information technique. Prediction ability of the network was tested at two different levels of inter-subject generalization. The WNN predictions were validated against outputs from multi body dynamics method in terms of normalized root mean square error (NRMSE (%)) and cross correlation coefficient (ρ). Results showed that WNN can predict joint moments to a high level of accuracy (NRMSE < 10%, ρ > 0.94) compared to FFANN (NRMSE < 16%, ρ > 0.89). A generic WNN could also calculate joint moments much faster and easier than multi body dynamics approach based on GRFs and EMG signals which released the necessity of motion capture. It is therefore indicated that the WNN can be a surrogate model for real-time gait biomechanics evaluation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2013 Elsevier Ltd. This is an author produced version of a paper published in Expert Systems with Applications. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Joint moment prediction; Mutual information; Wavelet neural network; Artificial neural network; Ground reaction force; Marker trajectory |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Medical and Biological Engineering (iMBE) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Mar 2016 11:10 |
Last Modified: | 16 Jan 2018 06:29 |
Published Version: | http://dx.doi.org/10.1016/j.eswa.2013.11.003 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.eswa.2013.11.003 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:92792 |