Classification of Knee Abnormality Using sEMG Signals with Boosting Ensemble Approaches

Abstract

Knee problems, although increasing in the elderly, are one of the most important orthopedic problems that occur at any age and reduce the person's standard of living by making it difficult to move. In recent years, increasing in the use of surface Electromyography (sEMG) signals from muscles has highlighted the use of these signals in the detection of movement and movement disorders. In this study, sEMG signals, from patients with different knee abnormalities and healthy individuals, the muscles responsible for the bending (flexion) and stretching/extension (extension) movements of the knee (rectus femoris (RF), biceps femoris (FB), semitendinosus (ST), vastus medialis (VM)), recorded during gait, sitting, and standing were evaluated with some statistical-based features. Unlike the literature, the classification processes were alsoperformed for each muscle and each movement, and therefore the effect of the muscles on the classification performance was examined. The ensemble trees methods of Boosted and RUSboosted trees were used in the classification. The results show that the knee problem can be identified by using single muscle sEMG (RF) and single movement, with a performance about 92% for the movement of standing. The highest accuracy rate is obtained as 98.8% with Boosted Trees classifier for sitting by using all muscles sEMG signals.