Title :
Artificial neural network based gait patterns identification using neuromuscular signals and soft tissue deformation analysis of lower limbs muscles
Author :
Arosha Senanayake, S.M.N. ; Triloka, Joko ; Malik, Owais A. ; Iskandar, Mohammad
Author_Institution :
Fac. of Sci., Univ. Brunei Darassalam, Gadong, Brunei
Abstract :
The objective of this study is to investigate the use of electromyography (EMG) signals and video based soft tissue deformation (STD) analysis for identifying the gait patterns of healthy and injured subjects. The system includes a wireless surface electromyography (EMG) sensor unit and two video camera systems for measuring the neuromuscular activity of lower limb muscles, and a custom-developed artificial neural network based intelligent system software for identifying the gait patterns of subjects during walking activity. The system uses root mean square (RMS) value of EMG signals and soft tissue deformation parameter (STDP) as the input features. In order to estimate the STD during a muscular contraction while walking, flexible triangular meshes are built on reference points. The positions of these selected points are evaluated by applying the block matching motion estimation technique. Based on the extracted features, multilayer feed-forward backpropagation networks (FFBPNNs) with different network training functions were designed and their classification performances were compared. The system has been tested for a group of healthy and injured subjects. The results showed that FFBPNN with Levenberg-Marquardt training function provided better prediction behavior (98% overall accuracy) as compared to FFBPNN with other training functions for gait patterns identification based on RMS value of EMG and STDP.
Keywords :
backpropagation; electromyography; gait analysis; mean square error methods; medical signal processing; multilayer perceptrons; muscle; pattern classification; pattern matching; video cameras; video signal processing; EMG sensor unit; EMG signals; FFBPNN; Levenberg-Marquardt training function; RMS value; STDP; artificial neural network based gait pattern identification; block matching motion estimation technique; custom-developed artificial neural network based intelligent system software; electromyography signals; feature extraction; lower limb muscles; multilayer feedforward backpropagation networks; muscular contraction; network training functions; neuromuscular activity; neuromuscular signals; root mean square value; soft tissue deformation parameter; video based STD analysis; video based soft tissue deformation analysis; video camera systems; wireless surface electromyography sensor unit; Artificial neural networks; Biological tissues; Cameras; Electromyography; Feature extraction; Legged locomotion; Muscles; artificial neural network; electromyography; gait patterns; soft tissue deformation;
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
DOI :
10.1109/IJCNN.2014.6889899