Robot arm control method using forearm EMG signals

Abstract

With the continuous improvement of control technology and the continuous improvement of people’s living standards, the needs of disabled people for high-quality prosthetics have become increasingly strong. A control method of robotic arm based on surface electromyography signal (sEMG) of forearm is proposed. Firstly, the 16-channel EMG data of the forearm is obtained via the multi-channel EMG acquisition instrument and the electrode cuff as input signals, the features are extracted, then the gestures are classified and identified by the support-vector machine (SVM) algorithm, and the signals are finally transmitted to the robotic arm, so that people can teleoperate the robotic arm via sEMG signals in real time. Reduce the number of channels to lower the cost while ensuring a high and usable recognition rate. Experiments were performed by collecting EMG signals from the forearm surface of eight healthy volunteers. The experimental results show that the system’s overall gesture recognition accuracy rate can reach up to 90%, and the system responds fast, laying a good foundation for manipulating artificial limbs in the future.