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Design and Implementation of Prosthetic Hand Control Using Myoelectric Signal

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@article{IJASEIT4887,
   author = {Akif Rahmatillah and Limpat Salamat and Soegianto Soelistiono},
   title = {Design and Implementation of Prosthetic Hand Control Using Myoelectric Signal},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {4},
   year = {2019},
   pages = {1231--1237},
   keywords = {prosthetic hand; electromyography; artificial neural network; myoelectric signal.},
   abstract = {

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%.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4887},    doi = {10.18517/ijaseit.9.4.4887} }

EndNote

%A Rahmatillah, Akif
%A Salamat, Limpat
%A Soelistiono, Soegianto
%D 2019
%T Design and Implementation of Prosthetic Hand Control Using Myoelectric Signal
%B 2019
%9 prosthetic hand; electromyography; artificial neural network; myoelectric signal.
%! Design and Implementation of Prosthetic Hand Control Using Myoelectric Signal
%K prosthetic hand; electromyography; artificial neural network; myoelectric signal.
%X 

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%.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4887 %R doi:10.18517/ijaseit.9.4.4887 %J International Journal on Advanced Science, Engineering and Information Technology %V 9 %N 4 %@ 2088-5334

IEEE

Akif Rahmatillah,Limpat Salamat and Soegianto Soelistiono,"Design and Implementation of Prosthetic Hand Control Using Myoelectric Signal," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 4, pp. 1231-1237, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.4.4887.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Rahmatillah, Akif
AU  - Salamat, Limpat
AU  - Soelistiono, Soegianto
PY  - 2019
TI  - Design and Implementation of Prosthetic Hand Control Using Myoelectric Signal
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 4
Y2  - 2019
SP  - 1231
EP  - 1237
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - prosthetic hand; electromyography; artificial neural network; myoelectric signal.
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%.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4887 DO - 10.18517/ijaseit.9.4.4887

RefWorks

RT Journal Article
ID 4887
A1 Rahmatillah, Akif
A1 Salamat, Limpat
A1 Soelistiono, Soegianto
T1 Design and Implementation of Prosthetic Hand Control Using Myoelectric Signal
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 4
YR 2019
SP 1231
OP 1237
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 prosthetic hand; electromyography; artificial neural network; myoelectric signal.
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%.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4887 DO - 10.18517/ijaseit.9.4.4887