EMG controlled low cost prosthetic arm

Electromyography (EMG) signals have been extensively used as a control signal in robotics, rehabilitation and health care. In this paper, cost effective design of prosthetic hand using EMG control is presented. Signal amplification and filtering is the primary step in surface EMG signal processing and application systems. Quality of the acquired EMG signal depends on the amplifiers and filters employed. Single channel continuous EMG signal has been acquired from the users arm for various hand movements. The acquired signal is passed through various stages of filters and amplifiers for amplification and noise reduction. The conditioned analog signal is converted into digital samples. After the signal acquisition process, features are extracted from the acquired signal and the extracted features are reduced to minimize the number of computations. These reduced feature parameters are used to classify the signal for different hand movements. Once the classifier identifies the intended motion, the control signal will be generated and given to the motors in the prosthetic hand to perform the intended movements. Experiments were done to find the efficiency of the developed system and it is found that this system can give basic movements at a very low cost.

[1]  Patrick van der Smagt,et al.  Surface EMG in advanced hand prosthetics , 2008, Biological Cybernetics.

[2]  D. Caldwell,et al.  Task-Orientated Biofeedback System for the Rehabilitation of the Upper Limb , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[3]  Abdulhamit Subasi,et al.  Classification of EMG signals using combined features and soft computing techniques , 2012, Appl. Soft Comput..

[4]  Silvestro Micera,et al.  On the Shared Control of an EMG-Controlled Prosthetic Hand: Analysis of User–Prosthesis Interaction , 2008, IEEE Transactions on Robotics.

[5]  Rubita Sudirman,et al.  Features extraction of electromyography signals in time domain on biceps brachii muscle , 2013 .

[6]  Congli Mei,et al.  Pattern Recognition of Eight Hand Motions Using Feature Extraction of Forearm EMG Signal , 2014 .

[7]  Abdulhamit Subasi,et al.  Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders , 2013, Comput. Biol. Medicine.

[8]  Ganesha Udupa,et al.  AN INTELLIGENT PLANT EMG SENSOR SYSTEM FOR PRE- DETECTION OF ENVIRONMENTAL HAZARDS , 2013 .

[9]  Sheroz Khan,et al.  High Quality Acquisition of Surface Electromyography – Conditioning Circuit Design , 2013 .

[10]  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..

[11]  Luay Fraiwan,et al.  Real time virtual prosthetic hand controlled using EMG signals , 2011, 2011 1st Middle East Conference on Biomedical Engineering.

[12]  Sabri Koçer,et al.  Classification of EMG Signals Using PCA and FFT , 2005, Journal of Medical Systems.

[13]  .. S. Day Important Factors in surface EMG measurement By Dr , 2002 .

[14]  Guido Bugmann,et al.  Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography , 2013, IEEE Journal of Biomedical and Health Informatics.

[15]  Seul Jung,et al.  Line tracking control of a mobile robot using EMG signals from human hand gestures , 2015, 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).