Human shoulder motion extraction using EMG signals

This paper suggests a joint angle extraction method for shoulder flexion movement in a sagittal body plane. Surface electromyogram (EMG) signals measured at trapezius muscle are utilized for joint angle extraction in real-time. The relationship between the shoulder motion and the measured EMG signal can be modeled using a spring-damper pendulum model. In the suggested model, the EMG signal is described as the function of the shoulder flexion joint angle and its derivative with dynamic model parameters. In preprocessing procedures, the raw EMG signals are processed by taking root mean square (RMS) and filtering out noises with low-pass filter (LPF). Also, the model parameters are determined through an optimization for the measured EMG signals and their corresponding real joint angles measured from vision tracker system. A part of the model parameters are modified with two different slopes when the shoulder joint angle exceeds 90 degrees. For the main procedures, the moving average filter-based model dynamics is implemented to extract the shoulder angle, here, the moving average filtering is performed with the varying window size to reduce the oscillations of the EMG signals caused by the muscle fatigue. Finally, we show the effectiveness the suggested method through several experiments.