Recognition of Movements Through Dynamic Electromyographic Signals

Abstract

The recognition of movements through electromyographic (EMG) signals is critical for myoelectric control systems. Performance of these systems depend on processing methods and protocols used to extract the EMG signals. The aim of this study is to evaluate the performance of classification of a kinematic recognition system based on dynamic EMG signals. For this, a correlation analysis between dynamic EMG signals and kinematic features of movements is realized, and then, a kinematic recognition system based on dynamic EMG signals is implemented. Dynamic EMG signals from forearm muscles during finger flexion movements were recorded and analyzed by using an amplitude estimator. Linear and no-linear correlations between EMG amplitudes and kinematic features were found. Then, a step of classification based on discriminant analysis was implemented to categorize the finger movements in multiple kinematic states. The accuracy of classifications were 95%, 88%, 81% and 76% for two, three, four and five states respectively, and by using a simple-channel recording and an EMG amplitude estimator. The results of this study demonstrate that it is possible to improve aspects of “intuitiveness” through dynamic EMG evoked by natural and more intuitive movements.