Signal processing of EMG signal for continuous thumb-angle estimation

Human hand functions range from precise-minute handling to heavy and robust movements. Developing an artificial thumb which can mimic the actions of a real thumb precisely is a major achievement. Despite many efforts dedicated to this area of research, control of artificial thumb movements in resemblance to our natural movement, still poses as a challenge. Most of the development in this area is based on discontinuous thumb position control, which makes it possible to recreate several of the most important functions of the thumb but does not result in total imitation. The paper looks into the classification of Electromyogram (EMG) signals from thumb muscles for the prediction of thumb angle during flexion motion. For this purpose, an experimental setup is developed to measure the thumb angle throughout the range of flexion and simultaneously gather the EMG signals. Further various different features are extracted from these signals for classification and the most suitable feature set is determined and applied to different classifiers. A `piecewise-discretization´ approach is used for continuous angle prediction. The most determinant features are found to be the 2nd order Auto-regressive (AR) coefficients with Simple Square Integral (SSI) giving an accuracy of 85.41% in average while the best classification method is Support Vector Machine (SVM - with Puk kernel) with an average accuracy of 86.53%.