TY - JOUR
T1 - Design and implementation of prosthetic hand control using myoelectric signal
AU - Rahmatillah, Akif
AU - Salamat, Limpat
AU - Soelistiono, Soegianto
N1 - Publisher Copyright:
© 2019 Insight Society.
PY - 2019
Y1 - 2019
N2 - 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%.
AB - 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%.
KW - Artificial neural network
KW - Electromyography
KW - Myoelectric signal
KW - Prosthetic hand
UR - http://www.scopus.com/inward/record.url?scp=85076620161&partnerID=8YFLogxK
U2 - 10.18517/ijaseit.9.4.4887
DO - 10.18517/ijaseit.9.4.4887
M3 - Article
AN - SCOPUS:85076620161
SN - 2088-5334
VL - 9
SP - 1231
EP - 1237
JO - International Journal on Advanced Science, Engineering and Information Technology
JF - International Journal on Advanced Science, Engineering and Information Technology
IS - 4
ER -