Robot arm control method using forearm EMG signals

Minjie Chen,Honghai Liu,J Joo

doi:10.1051/matecconf/202030904007

Minjie Chen, Honghai Liu + Show 1 more

Open Access

https://doi.org/10.1051/matecconf/202030904007

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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.

Highlights

In the past few decades, robotics has developed unprecedentedly and is widely used in assisted rehabilitation for the disabled
After the subject rests for 10 minutes (Note: the electrode sleeve cannot be untied to prevent the electrode displacement from shifting), the above 8 gestures are randomly performed, and the corresponding surface EMG data is recorded in real time and passed the trained model
This study designed a manipulator control system based on forearm surface EMG signals

Summary

Introduction

In the past few decades, robotics has developed unprecedentedly and is widely used in assisted rehabilitation for the disabled. The EMG signal has the characteristics of simple acquisition and direct correlation with the contraction and relaxation of muscle fibers, so it is widely used in clinical medicine, rehabilitation medicine, artificial intelligence and other aspects [6]. Surface electromyography signal (sEMG) is a bioelectrical signal generated by the neuromuscular activity recorded on the surface of human skeletal muscle through electrodes [7]. The sEMG signal reflects the sum of the action potentials released during the muscle fiber activity near the recording electrode. The good interactive experience of sEMG signals has a broad application prospect in the field of prosthetic control. It is natural and intuitive for amputees to control prosthetic hands with sEMG. Researchers are committed to researching new types of multifunctional human prosthetic hands, such as Southampton-REMEDI Hand [8], RTR 2 [9], MANUS [10], Karlsruhe Hand [11] and so on

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