Development of SEMG Gripper and Matlab based Acquisition System

In this paper the system is proposed which will detect, amplify and digitize the SEMG signals which can be further used for feature extraction. SEMG signals were recorded through two non-invasive electrodes placed back near elbow and then various features like RMS, Variance, Median and standard deviation have been calculated in matlab .The features were calculated for the raw signals and then Hilbert Transform is applied to the signals for envelope detection and further processing. The system is then interfaced with a gripper which will move according to the actual movement of fingers. The SEMG gripper along with matlab acquisition system finds its application in prosthetic hands.

[1]  C. J. Luca,et al.  SURFACE ELECTROMYOGRAPHY : DETECTION AND RECORDING , 2022 .

[2]  Hong Liu,et al.  High performance DSP/FPGA controller for implementation of HIT/DLR dexterous robot hand , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[3]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[4]  Patrick van der Smagt,et al.  Learning EMG control of a robotic hand: towards active prostheses , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[5]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[6]  Rajesh P. N. Rao,et al.  Real-Time Classification of Electromyographic Signals for Robotic Control , 2005, AAAI.

[7]  Hong Liu,et al.  FPGA based hardware architecture for HIT/DLR hand , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.