Experimental Detection of Muscle Atrophy Initiation Using sEMG Signals

Atrophy is one of the most common consequences of muscle disorder. This could be a result of both myopathy and neuropathy. Muscle atrophy becomes more possible as people age. As a result of this disorder, the amount and size of muscle fibers decrease, therefore a person cannot produce high amount of force in his/her muscles, leading to difficulties in handling daily activities. The main purpose of this research is to find a way to predict this disorder. In this study the force classification was used for the atrophy disorder detection. The results show that different classifiers and features from the proposed ones, work for this purpose. To approach this goal, data were collected by recording surface EMG (sEMG) signals. Processing the recorded signals, best features with respect to more accuracy and less calculation complexity were selected and reported. After extracting the features from each patient with using different types of classifiers including LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis) and SVM (Support Vector Machine), the best approach to separate normal and atrophic people was investigated. It is found that unlike the proposed features such as MAV (Mean Absolute Value), SSC (Slope Sign Change) and WL (Waveform Length) in upper limb movement classification, three features WL, WAMP (Wilson Amplitude) (time domain features) and MNP (Mean Power) (frequency domain feature) show better performance for atrophy characterization. The results show that these features well predict the detection of biceps atrophy.