Realization of robust real time robotic arm control system based on EMG signal

2015 ◽  
Author(s):  
T. Puttasakul ◽  
M. Sangworasil ◽  
T. Matsuura
Keyword(s):  
Control System ◽  
Real Time ◽  
Robotic Arm ◽  
Emg Signal ◽  
Robotic Arm Control

scholarly journals Robotic Arm Control System Based on Brain-Muscle Mixed Signals

2021 ◽  
Author(s):  
Li-Wei Cheng ◽  
Duan-Ling Li ◽  
Gong-Jing Yu ◽  
Zhong-Hai Zhang ◽  
Shu-Yue Yu
Keyword(s):  
Control System ◽  
Real Time ◽  
Feature Recognition ◽  
Robotic Arm ◽  
Real Time Control ◽  
Existing Problems ◽  
Time Control ◽  
Emg Signal ◽  
Extended Application ◽  
Robotic Arm Control

Abstract Aiming at the existing problems of BCI (brain computer interface), such as single input signal source, low accuracy of feature recognition, and less output control instructions, this paper proposes a robotic arm control system based on EEG (electroencephalogram) and EMG (electromyogram) mixed signals. The system flow is as follows: Firstly, the EMG signal of the unilateral arm and the EEG signal of the left and right hand motor imagery is collected synchronously. Then the collected EEG and EMG signals are extracted and classified, and the corresponding classification instructions are obtained. Finally, the multi-instruction real-time control of the robotic arm is realized under the classification instruction. The experimental verification results show that: The 10 subjects all realized the real-time multi-command control of the robotic arm, and the average recognition accuracy of each action reached more than 94%. The proposed system enriches the diversity of hybrid BCI and provides a theoretical basis and application foundation for the extended application of BCI in robotic arm control.

Real-time Robotic Arm Control Embedded System using Hand Gestures

2021 ◽  
Vol 19 (11) ◽  
pp. 45-53
Author(s):  
Chung-Geun Kim ◽  
Eun-Su Kim ◽  
Jae-Wook Shin ◽  
Bum-Yong Park
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Robotic Arm ◽  
Hand Gestures ◽  
Robotic Arm Control

Hybrid EEG-EMG Based Brain Computer Interface (BCI) System For Real-Time Robotic Arm Control

2018 ◽  
Vol 10 (1) ◽  
pp. 35-40 ◽  
Author(s):  
Saad Abdullah ◽  
◽  
Muhammad A. Khan ◽  
Mauro Serpelloni ◽  
Emilio Sardini ◽  
...  
Keyword(s):  
Real Time ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Robotic Arm ◽  
Robotic Arm Control ◽  
Bci System

Using EMG Signals to Remotely Control a 3D Industrial Robotic Arm

2019 ◽  
Author(s):  
Wafa Batayneh ◽  
Ahmad Bataineh ◽  
Samer Abandeh ◽  
Mohammad Al-Jarrah ◽  
Mohammad Banisaeed ◽  
...  
Keyword(s):  
Real Time ◽  
Degrees Of Freedom ◽  
Control Device ◽  
Robotic Arm ◽  
Measurement Unit ◽  
Software Interface ◽  
Emg Signal ◽  
Hardware Interface ◽  
Gesture Control ◽  
Arm Motion

Abstract In this paper, a muscle gesture computer Interface (MGCI) system for robot navigation Control employing a commercial wearable MYO gesture Control armband is proposed. the motion and gesture control device from Thalamic Labs. The software interface is developed using LabVIEW and Visual Studio C++. The hardware Interface between the Thalamic lab’s MYO armband and the robotic arm has been implemented using a National Instruments My RIO, which provides real time EMG data needed. This system allows the user to control a three Degrees of freedom robotic arm remotely by his/her Intuitive motion by Combining the real time Electromyography (EMG) signal and inertial measurement unit (IMU) signals. Computer simulations and experiments are developed to evaluate the feasibility of the proposed System. This system will allow a person to wear this/her armband and move his/her hand and the robotic arm will imitate the motion of his/her hand. The armband can pick up the EMG signals of the person’s hand muscles, which is a time varying noisy signal, and then process this MYO EMG signals using LabVIEW and make classification of this signal in order to evaluate the angles which are used as feedback to servo motors needed to move the robotic arm. A simulation study of the system showed very good results. Tests show that the robotic arm can imitates the arm motion at an acceptable rate and with very good accuracy.

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