Marcelo Herrera González

Gustavo Adolfo Martínez Hernández

Jose Luis Rodríguez Sotelo

Oscar Fernando Avilés Sánchez


In this article a methodology for a medical diagnostic decision support system to assess knee injuries is proposed. Such methodology takes into account that these types of injuries are common and arise due to different causes. Therefore, the physician’s diagnostic and treatment may lead to expensive and invasive tests depending on his medical criteria. This system uses a surface Electromyographic (sEMG) and goniometric signals that are processed with signal analysis methods in time-frequency space through a spectrogram and a wavelet transform. Artificial neural networks are used as a learning technique by having a multilayer perceptron. EMG signals were measured in four external and internal muscles associated to the joint through flexion and extension assessments. These tests also registered the goniometric measures of the sagittal plane. This system shows above 80% of effectiveness as a performance measure that makes it an objective measure leading to help the physician in his diagnosis.



Knee injury, sEMG, ANN, goniometry, transformed wavelet

How to Cite
Herrera González, M., Martínez Hernández, G., Rodríguez Sotelo, J., & Avilés Sánchez, O. (2015). Knee functional state classification using surface electromyographic and goniometric signals by means artificial neural networks. Ingenieria Y Universidad, 19(1), 51-66. https://doi.org/10.11144/Javeriana.iyu19-1.kfsc
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