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Building accurate classifiers is difficult when using data that is skewed or imbalanced which is typical of real world data sets. Two popular approaches that have been applied for improving classification accuracy and statistical comparisons of imbalanced data sets are: synthetic minority over-sampling technique (SMOTE) and propensity score matching (PSM). A novel sampling approach is introduced referred...
Systems based on Surface Electromyography (sEMG) signals require some form of machine learning algorithm for recognition and classification of specific patterns of muscle activity. These algorithms vary in terms of the number of signals, feature selection, and the classification algorithm used. In our previous work, a technique for recognizing muscle patterns using a single sEMG signal, called Guided...
Rehabilitation devices, prosthesis and human machine interfaces are among many applications for which surface electromyographic signals (sEMG) can be employed. Systems reliant on these muscle-generated electrical signals require various forms of machine learning algorithms for specific signature recognition. Those systems vary in terms of the signal detection methods, the feature selection and the...
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