Mapping of EMG signal to hand grip force at varying wrist angles

Limb loss is a growing problem in Malaysia and the rest of the world due to the increasing number of industrial accidents, diseases and armed conflicts. After a tragic incident resulting in an amputation or paralysis, the disabled individual needs to be assisted with all possible technological means to improve his quality of life. A cybernetic prosthesis is a device which can greatly assist individuals with hand disabilities by enabling them to have some of the hand capabilities of an able bodied individual. The central nervous system which consists of the brain and spine governs hand grip force and hand movement in the human body by spatial and temporal motor unit recruitments. Electromyogram (EMG) is an electrical biological signal that can be measured from the skin surface and consists of the summation of Motor Unit Action Potentials (MUAP). Hand grip strength, wrist extension and wrist flexion are hand functions which result from the forearm muscle activity and are used in a wide range of daily tasks. Extracting hand grip force and wrist angle information from forearm EMG signals is useful to be used as inputs for the control of cybernetic prostheses. By establishing the relationship between forearm EMG and hand grip force/wrist angles, the prosthetic hand can be controlled in a manner that is customized to an amputee's intent. In this research work, a myoelectric interface which consists of an electronic conditioning circuit to measure EMG signals and the software to record and process the EMG signals was developed. Experimental training and testing data sets from five subjects were collected to investigate the relationship between forearm EMG, hand grip force and wrist angle simultaneously.

[1]  Michael Norris,et al.  Design and development of medical electronic instrumentation : a practical perspective of the design, construction, and test of medical devices , 2004 .

[2]  Gary Kamen,et al.  Essentials of Electromyography , 2009 .

[3]  F. Mohd-Yasin,et al.  Techniques of EMG signal analysis: detection, processing, classification and applications , 2006, Biological Procedures Online.

[4]  Hong Liu,et al.  Estimation of hand grasp force based on forearm surface EMG , 2009, 2009 International Conference on Mechatronics and Automation.

[5]  Malek Adjouadi,et al.  Design of an Electrical Prosthetic Gripper using EMG and Linear Motion Approach , 2004 .

[6]  Andrew Jackson,et al.  Learning a Novel Myoelectric-Controlled Interface Task , 2008, Journal of neurophysiology.

[7]  Rajesh P. N. Rao,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. 1 Online Electromyographic Control of a Robotic , 2022 .

[8]  K. Hashtrudi-Zaad,et al.  Rowing stroke force estimation with EMG signals using artificial neural networks , 2005, Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005..

[9]  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..

[10]  R. Naemi,et al.  Routledge Handbook of Biomechanics and Human Movement Science , 2008 .

[11]  Hong Liu,et al.  EMG Control for a Five-fingered Prosthetic Hand Based on Wavelet Transform and Autoregressive Model , 2006, 2006 International Conference on Mechatronics and Automation.

[12]  J. Cacioppo,et al.  Handbook Of Psychophysiology , 2019 .

[13]  John G. Webster,et al.  The Measurement, Instrumentation and Sensors Handbook , 1998 .

[14]  H. Dong-mei,et al.  Measurement System for Surface Electromyogram and Handgrip Force Based on LabVIEW , 2009 .

[15]  Saravanan N Madras,et al.  Biosignal Based Human-Machine Interface for Robotic Arm , 2007 .

[16]  A. Kargov,et al.  The FLUIDHAND III: A Multifunctional Prosthetic Hand , 2009 .

[17]  Kevin B. Englehart,et al.  A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.

[18]  M. Shikida,et al.  Glove Type of Wearable Tactile Sensor Produced by Artificial Hollow Fiber , 2007, TRANSDUCERS 2007 - 2007 International Solid-State Sensors, Actuators and Microsystems Conference.

[19]  K.R. Wheeler,et al.  Gesture-based control and EMG decomposition , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).