Performance evaluation of various classifiers for predicting knee angle from electromyography signals

This paper proposes a classification-based knee angle prediction from myoelectric signals. Surface electromyographic signals were recorded from four muscles in the lower limb while performing the task of standing up from a chair and sitting down on the chair. Knee angle was measured using a goniometer and quantised into five levels/classes. The surface electromyographic signals were segmented using overlapped windowing. Fifteen features per muscle were extracted and fed to the classifier. The classifier predicts the class of the knee angle at a particular instant This study examines the performance of linear discriminant analysis, Naive Bayes, k-nearest neighbour, and support vector machine classifiers. The support vector machine classifier with a quadratic kernel performed best, with a classification accuracy of 92.2 +/- 2.2%, a sensitivity of 90.19 +/- 3.06%, a specificity of 98.11 +/- 0.63%, and 89.38 +/- 3.0% precision.