Recognizing Lower Limb Pathology Thought An EMG Classification Model

Abstract

This paper proposes a tool for studying the relationship of surface electromyography between healthy people and someone with some pathology in the lower limb. Support Vector Machine SVM is used to classify electro myographic signals because models are robust to overfitting. For the lower limb, analysis has been taken the EMG Dataset from UCI Machine Learning [1]. This database contains signals from 11 subj ects with knee abnormality and 11 normally, previously diagnosed by a professional. They undergo three movements to analyze the behavior associated with the lower limb, gait, leg extension from a sitting position, and flexion of the leg up. Analysis was divided into 4 stages: preprocessing, features extraction, training, and validation. Several conventional electromyography features are used in performance comparison with it combined with Enhanced features. Based on results obtained by algorithms with different Kernel the Support Vector Machine model provided by MATLAB® Classification Learner app achieves the highest accuracy of 96.7%.