Recognition of multiple arm movements from EMG signals

Controlling smart forearm and hand myoelectric prostheses requires reliable recognition of one type of arm movement among several other types. In this study, using real EMG signals of 4 types of arm movements, we searched the best multiclass classifier using SVM. Based on our previous results, features were first extracted from the relevant intrinsic mode functions (IMFs), namely the first two of each signal. Then, relevant feature vectors of short lengths (≤ 8) were selected.Two approaches of multiclass classification were tested: one vs one (OVO) and one vs rest (OVR) using different kernel functions. By varying the SVM cost C and the kernels parameters γ and n and using 100 cross-validation iterations, we found that the best classifier is OVO-RBF associated to the values (C, γ) = (1, 1.1) which gave mean values of accuracy =94.4%, sensitivity=94.44%, specificity=98.27% and AUC=0.96. Moreover, the use of SMOTE to increase the size of the database improved classifications results and all the kernels manage to discriminate between the four movements.