Electromyography (EMG) signals have been extensively used for identification of finger movements, hand gestures and physical activities. In the classification of EMG signals, the performance of the classifier is widely determined by the feature extraction methods. Thus, plenty of feature extraction methods based on time, histogram and frequency domain have been reported in literature. However, these methods have several drawbacks such as high time complexity, high computation demand and user supplied parameters. To overcome these deficiencies, in this work, a new feature extraction method has been proposed to classify EMG signals taken from two different data sets finger movements (FM) and physical actions (PA). While FM data set includes 14 different finger movements, PA data set involves 20 different physical activities. The proposed method is based on numerical fractional integration of time series EMG signals with different fractional-orders. K Nearest Neighborhood (KNN) classifier with 8-fold cross validation has been employed for prediction of EMG signals. The derived fractional features can give better results than the two commonly used time domain features, notably, mean absolute value (MAV) and waveform length (WL) in terms of accuracy. The experimental results are also supported by statistical analysis results.