Design and Implementation of Prosthetic Hand Control Using Myoelectric Signal

Akif Rahmatillah, Limpat Salamat, Soegianto Soelistiono


Amputation is a medical procedure that is required to cut part of or all of the extremity, i.e. upper limbs or lower limbs. In the final phase of the procedure, patients have to adapt to their new condition including the use of prostheses. Nowadays, Prosthetic hand have had a lot of improvements that enable patients to do normal activities by exploiting their myoelectric signal. This study has a goal to produce prosthetic hand that can respond to patient generating myoelectric signal. Three muscle leads (2 on  muscle flexor digitorum, 1 on muscle extensor digitorum) were processed by 3 channels surface electromyography (sEMG) that contain of instrument amplifier i.e. high-pass filter, rectifier, and notch filter. Myoelectric signal is processed to extraction feature and classified by artificial neural network (ANN) that had been offline-trained before and had 21 neurons input layer, 10 neurons hidden layer, and 3 neurons output layer to detect 3 hand movements, i.e. grasping, pinch, and open grasp. ANN and prosthetic hand control was embedded on Arduino Due microcontroller so that the system could be used in stand-alone and real time mode. The results of the testing from 4 research subjects shown that the hand prostheses system had success rate of 87% – 91%.


prosthetic hand; electromyography; artificial neural network; myoelectric signal.

Full Text:



K. Lamb. (2016), Amputation. [Online]. Available:

Ottobock. (2016), Rehabilitation. [Online]. Available:

R. LeMoyne, Advance for Prosthetic Technology: From Historical Perispective to Current Status to Future Application, Tokyo, Springer, 2016.

L. McLelan and R. N. Scott, Powered Upper Limb Prostheses: Control, Implementation and Clinnical Application, New York, Springer, 2016.

P. Slade, A. Akhtar, M. Nguyen and T. Bretl, "Tact: Design and performance of an open-source, affordable, myoelectric prosthetic hand," 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, 2015, pp. 6451-6456.

P. Geethanjali and K. K. Ray, "A Low-Cost Real-Time Research Platform for EMG Pattern Recognition-Based Prosthetic Hand," in IEEE/ASME Transactions on Mechatronics, vol. 20, no. 4, pp. 1948-1955, Aug. 2015.

F. Riillo, L.R. Quitadamo, F. Cavrini, E. Gruppioni, C.A. Pinto, N. Cosimo Pastò, L. Sbernini, L. Albero and G. Saggio, "Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees",in Biomedical Signal Processing and Control, Vol. 14, pp 117-125, 2014.

U. Baspinar, H. Selcuk Varol, and V. Y. Senyurek, " Performance Comparison of Artificial Neural Network and Gaussian Mixture Model in Classifying Hand Motions by Using sEMG Signals ", in Biocybernetics and Biomedical Engineering, Vol. 33, Issue 1, pp 33-45, 2013.

R. N. Khushaba, S. Kodagoda, M. Takruri, and G. Dissanayake, " Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals ", in Expert Systems with Applications, Vol. 39, Issue 12, pp. 10731-10738, 2012.

L. Bitjoka, M. Ndje, A.T. Boum, J.S. Manguele," Implementation of Quadratic Dynamic Matrix Control on Arduino DUE ARM Cortex M3+ Microcontroller Board ", in Journal of Engineering and Technology, Vol. 6, pp. 682 - 695, 2017 .

M. R. Ahsan, M. I. Ibrahimy and O. O. Khalifa, "Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN)," 2011 4th International Conference on Mechatronics (ICOM), Kuala Lumpur, 2011, pp. 1-6.

M. N. Mohd Nor,R. Jailani,N. M. Tahir,Ihsan Mohd Yassin,Zairi Ismael Rizman and Rahmat Hidayat,"EMG Signals Analysis of BF and RF Muscles In Autism Spectrum Disorder (ASD) During Walking," International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 5, pp. 793-798, 2016. [Online]. Available:



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development