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Controlling an Upper-Limb Rehabilitation Robot by EMG Signals
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This paper presents a method for controlling the motion of a 6-DOF upper limb rehabilitation robot based on electromyography (EMG) signal. A kinematic model has been determined by Denavit - Hartenberg (D-H) method. Due to the complexity of the robot mechanical structure, the inverse kinematics is addressed using the Jacobian method. Besides, the EMG signal is a physiological signal generated during muscle contraction, which is collected from a low-cost sensor interacted on the human arm, but noise is inevitable in the extracted data. Therefore, the method treats EMG signal noise using Butterworth filter and proposes a method to predict elbow joint angle based on EMG using zero crossing and Kalman filter. Finally, the proposed method is evaluated through simulation on MATLAB Simulink and experiments on a 6-DOF robot arm model. The experimental results show that the EMG signal processing method proposed is significantly effective and the upper limb rehabilitation robot based on EMG signals is feasible.
Ho Chi Minh City University of Technology and Education
Title: Controlling an Upper-Limb Rehabilitation Robot by EMG Signals
Description:
This paper presents a method for controlling the motion of a 6-DOF upper limb rehabilitation robot based on electromyography (EMG) signal.
A kinematic model has been determined by Denavit - Hartenberg (D-H) method.
Due to the complexity of the robot mechanical structure, the inverse kinematics is addressed using the Jacobian method.
Besides, the EMG signal is a physiological signal generated during muscle contraction, which is collected from a low-cost sensor interacted on the human arm, but noise is inevitable in the extracted data.
Therefore, the method treats EMG signal noise using Butterworth filter and proposes a method to predict elbow joint angle based on EMG using zero crossing and Kalman filter.
Finally, the proposed method is evaluated through simulation on MATLAB Simulink and experiments on a 6-DOF robot arm model.
The experimental results show that the EMG signal processing method proposed is significantly effective and the upper limb rehabilitation robot based on EMG signals is feasible.
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