By Bita Mokhlesabadifarahani, Vinit Kumar Gunjan
Neuro-muscular and musculoskeletal issues and accidents hugely have an effect on the life-style and the movement skills of somebody. This short highlights a scientific process for detection of the extent of muscle strength declining in musculoskeletal and Neuro-muscular problems. The neuro-fuzzy procedure is knowledgeable with 70 percentage of the recorded Electromyography (EMG) bring to a halt window after which used for class and modeling reasons. The neuro-fuzzy classifier is demonstrated compared to another famous classifiers in category of the recorded EMG indications with the 3 states of contractions comparable to the extracted good points. diverse constructions of the neuro-fuzzy classifier also are relatively analyzed to discover the optimal constitution of the classifier used.
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Extra resources for EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction
Example text
For neuro-fuzzy classifier, a combination of least squares and back propagation method was used as learning algorithm. Trapezoidal and Gaussian membership functions are commonly used as shape of fuzzy sets of inputting nodes. Number of 2–4 membership functions is suggested for each variable in EMG signal modeling problem. 6 shows results of implementing five types of classifiers for classification of EMG signals according to extracted features. Due to difference between muscles power of two groups of gender, male and female, we separated males and females in analysis of their EMG signals of mentioned muscles.
Therefore, each window (with 20,000 samples) is split into 10 sub-windows each one with 2,000 samples. Seventy percent of sub-windows are still considered for training purpose (7 sub-windows) and rests for testing purpose (3 sub-windows). For evaluating classifier, mean-squared error is used which is most common criterion defined as below: MSE = 1 N N (yi − y¯ i ) i=1 where yi and y¯ i are real and desired outputs of network, respectively, and N is total number of samples. MSE of training process shows trainability of system, and MSE of testing samples indicates system’s modeling capability.
When premise parameters are not fixed, search space becomes larger and convergence of training becomes slower. A hybrid algorithm combining least squares method and gradient descent method is adopted to solve this problem. Hybrid algorithm is composed of a forward pass and a backward pass. Least squares method (forward pass) is used to optimize consequent parameters with premise parameters fixed. Once optimal consequent parameters are found, backward pass starts immediately. Gradient descent method (backward pass) is used to adjust optimally premise parameters corresponding to fuzzy sets in input domain.