TY - JOUR
T1 - Hand movement classification using burg reflection coefficients
AU - Ramírez-Martínez, Daniel
AU - Alfaro-Ponce, Mariel
AU - Pogrebnyak, Oleksiy
AU - Aldape-Pérez, Mario
AU - Argüelles-Cruz, Amadeo José
N1 - Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2019/2/1
Y1 - 2019/2/1
N2 - 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 for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the 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 the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.
AB - 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 for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the 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 the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.
KW - Classification algorithms
KW - Electromyography
KW - Feature selection
KW - Hand movement
KW - Health monitoring
KW - Machine learning
KW - Maximum entropy reflection coefficients
UR - http://www.scopus.com/inward/record.url?scp=85060515681&partnerID=8YFLogxK
U2 - 10.3390/s19030475
DO - 10.3390/s19030475
M3 - Artículo
C2 - 30682797
SN - 1424-8220
VL - 19
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 3
M1 - 475
ER -