Knee Functional State Classification Using Surface Electromyographic and Goniometric Signals by Means of Artificial Neural Networks

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 diagnosis 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.