Title of article :
Human lower extremity joint moment prediction: A wavelet neural network approach
Ardestani، نويسنده , , Marzieh Mostafavizadeh and Zhang، نويسنده , , Xuan and Wang، نويسنده , , Ling and Lian، نويسنده , , Qin and Liu، نويسنده , , Yaxiong and He، نويسنده , , Jiankang and Li، نويسنده , , Dichen and Jin، نويسنده , , Zhongmin، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2014
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.
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).
s 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.
Wavelet neural network , mutual information , Joint moment prediction , Ground reaction force , Marker trajectory , Artificial neural network
Journal title :
Expert Systems with Applications