TY - JOUR
T1 - Embedded system for upper-limb exoskeleton based on electromyography control
AU - Triwiyanto, Triwiyanto
AU - Pawana, I. Putu Alit
AU - Irianto, Bambang Guruh
AU - Indrato, Tri Bowo
AU - Wisana, I. Dewa Gede Hari
N1 - Publisher Copyright:
© 2019 Universitas Ahmad Dahlan.
PY - 2019
Y1 - 2019
N2 - A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
AB - A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
KW - Electromyography
KW - Embedded system
KW - Exoskeleton
KW - Feature extraction
KW - Zero crossing
UR - http://www.scopus.com/inward/record.url?scp=85076024917&partnerID=8YFLogxK
U2 - 10.12928/TELKOMNIKA.v17i6.11670
DO - 10.12928/TELKOMNIKA.v17i6.11670
M3 - Article
AN - SCOPUS:85076024917
SN - 1693-6930
VL - 17
SP - 2992
EP - 3002
JO - Telkomnika (Telecommunication Computing Electronics and Control)
JF - Telkomnika (Telecommunication Computing Electronics and Control)
IS - 6
ER -