Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Sanaz Ahmadzadeh; Hamid Reza Kobravi; Saeed Tosizadeh
Volume 8, Issue 3 , September 2014, , Pages 293-304
Abstract
Multiple muscle groups may be activated simultaneously during the most of activities. So, the appropriate muscle coordination must be emerged during a normal activity. Consequaently, for rehabilitation of movements such as hand writing and paiting in patients for example suffering from carpal channel ...
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Multiple muscle groups may be activated simultaneously during the most of activities. So, the appropriate muscle coordination must be emerged during a normal activity. Consequaently, for rehabilitation of movements such as hand writing and paiting in patients for example suffering from carpal channel syndrom or incomplete spinal cord injury, the correct muscle coordination patterns between the finger muscles and wrist muscles must be reestablished. So, in this paper a prediction methodology based on artificial neural networks (ANN) is proposed to approximate the Thumb fingure extensor and flexor muscles desired activation pattern during the hand writing and Painting. In the presented strategy, A nonlinear auto-regressive neural network (NARX), Recurrent Neural Network (RNN), Radial Basis Function (RBF), Multy Layer Perceptron (MLP) and an Adaptive-network-based fuzzy inference system (ANFIS) are trained to forecast the Extensor pollicis longus and Flexor pollicis brevis muscles activity of one thumb finger of hand using Extensor carpi radialis brevis and Flexor carpi ulnaris muscles activity of forearm. Quantitative evaluations show the promising performance of developed neural networks. Eight healthy volunteers participated in the experiments.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohsen Naji; Seyed Mohammad Firouzabadi; Sedighe Kahrizi
Volume 7, Issue 1 , June 2013, , Pages 13-20
Abstract
The collected electromyogram (EMG) signals from trunk musculature (e.g., rectus abdominis and external oblique muscle) are often contaminated with the heart muscle electrical activity (ECG). This paper introduces a novel method, the Empirical Mode Decomposition, for elimination of ECG contamination from ...
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The collected electromyogram (EMG) signals from trunk musculature (e.g., rectus abdominis and external oblique muscle) are often contaminated with the heart muscle electrical activity (ECG). This paper introduces a novel method, the Empirical Mode Decomposition, for elimination of ECG contamination from EMG signals. The method is compared to a Butterworth high pass filtering. Results obtained from the analysis of generated and experimental EMG signals show that our method outperforms the high pass filtering for elimination of ECG contamination from trunk EMG signals.