Ramírez-Martínez, D.; Alfaro-Ponce, M.; Pogrebnyak, O.; Aldape-Pérez, M.; Argüelles-Cruz, A.-J. Hand Movement Classification Using Burg Reflection Coefficients. Sensors2019, 19, 475.
Ramírez-Martínez, D.; Alfaro-Ponce, M.; Pogrebnyak, O.; Aldape-Pérez, M.; Argüelles-Cruz, A.-J. Hand Movement Classification Using Burg Reflection Coefficients. Sensors 2019, 19, 475.
Ramírez-Martínez, D.; Alfaro-Ponce, M.; Pogrebnyak, O.; Aldape-Pérez, M.; Argüelles-Cruz, A.-J. Hand Movement Classification Using Burg Reflection Coefficients. Sensors2019, 19, 475.
Ramírez-Martínez, D.; Alfaro-Ponce, M.; Pogrebnyak, O.; Aldape-Pérez, M.; Argüelles-Cruz, A.-J. Hand Movement Classification Using Burg Reflection Coefficients. Sensors 2019, 19, 475.
Abstract
Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input control of prosthetic devices has become a hot topic of research. Challenge of classifying this signals relies on the accuracy of the proposed algorithm and the possibility of its implementation on hardware. This paper consider the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction, and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to used time domain features. Sometimes, the feature extraction from electromyographic signals showed that procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as fewer traits as possible. Algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.
Keywords
electromyography; hand movement; health monitoring; maximum entropy reflection coefficients; classification algorithms; machine learning; feature selection
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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