Abstract
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 postprocessing. 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.
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Acknowledgment
The authors gratefully acknowledge the financial support provided by Escuela Politécnica Nacional for the development of the research project PIJ-16-13 ‘Clasificación de señales electromiográficas del brazo humano usando técnicas de reconocimiento de patrones y machine learning’.
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Motoche, C., Benalcázar, M.E. (2018). Real-Time Hand Gesture Recognition Based on Electromyographic Signals and Artificial Neural Networks. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_35
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