Abstract
This paper studies the continuous motion capture system of a robotic arm based on MYO. The MYO sensor can collect the electromyographic signal (sEMG) and the motion signal (IMU) of the arm during exercise, and filter the electromyographic signal, extract the features, and return the features. After the first transformation, the machine learning algorithm is used for model training. In order to verify the effect of the recognition of the EMG signal on the deep learning method, the accuracy comparison experiment with the BP neural network is also added. In addition, a two-link arm model was built, and the pose information of the end of the arm was obtained through Euler angle transformation, and then the end pose information of the robotic arm was obtained through coordinate mapping, and the end pose simulation was performed using Matlab software. The simulation results prove that the performance of the system meets expectations, and points out the direction for subsequent research.
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