Real-Time Hand Gesture Recognition Based on Electromyographic Signals and Artificial Neural Networks

In this paper, we propose a hand gesture recognition model based on superficial electromyographic signals. The model responds in approximately 29.38 ms (real time) with a recognition accuracy of 90.7%. We apply a sliding window approach using a main window and a sub-window. The sub-window is used to observe a segment of the signal seen through the main window. The model is composed of five blocks: data acquisition, preprocessing, feature extraction, classification and post-processing. For data acquisition, we use the Myo Armband to measure the electromyographic signals. For preprocessing, we rectify, filter, and detect the muscle activity. For feature extraction, we generate a feature vector using the preprocessed signals values and the results from a bag of functions. For classification, we use a feedforward neural network to label every sub-window observation. Finally, for postprocessing we apply a simple majority voting to label the main window observation.