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Machine learning-based novel approach to classify the shoulder motion of upper limb amputees

Amanpreet Kaur

Biocybernetics and Biomedical Engineering

2019

Vol. 39, no. 3

857--867

An upper limb amputation is a traumatic event that can seriously affect the person's capacity to perform regular tasks and can lead individuals to lose their confidence and autonomy. Prosthetic devices can be controlled via the acquisition and processing of electromyogram signal produced at the muscles fiber from the surface of the body with an array of an electrode placed on the residual limb. This paper presents the feasibility of classifying the different shoulder movements from around shoulder muscles of transhumeral amputees. The performance of a classifier is affected by the variation of Surface Electromyography (sEMG) signals due to the different categories of contraction. To avoid this, the wavelet transform and data transformation method are employed for features extraction from sEMG signals. Afterward five different supervised machine learning techniques viz. Support Vector Machines (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) are applied to determine the different classifiers accuracy. An effective combination of wavelet and RF achieves the best performance with a total classification accuracy of 98%.