TY - GEN
T1 - Elbow angle estimation for medical rehabilitation device based on EMG sensor with ARIMAX method
AU - Lita, Amelia
AU - Wibawa, I. Made Mas Dwiyana Prasetya
AU - Anggraini, Alinda
AU - Rahmatillah, Akif
AU - Rulaningtyas, Riries
AU - Putra, Alfian Pramudita
AU - Ain, Khusnul
N1 - Publisher Copyright:
© 2022 Author(s).
PY - 2022/9/20
Y1 - 2022/9/20
N2 - In the development of medical rehabilitation tools, muscle signals or electromyographic (EMG) are often used as feedback from the body in order to facilitate normal movement patterns. Through this signal, the force, torque, and angle can be estimated to produce the desired movement in the robot to assist patients in rehabilitation therapy. There had been various research done on estimating the extremities' joint angle, including the system identification method with Auto-Regressive Integrated Moving Average with Exogenous Input (ARIMAX) model structure. This research aims to model biceps brachii's EMG signal and the elbow joint angle using the ARIMAX model structure. In modeling, the order of the model was selected using the methods of minimizing the AIC (Akaike Information Criteria) and RMSE (Root Mean Square Error) values. The reported results showed the comparison of the outcome and model orders from three data sets with a frequency sampling of 47.6690 Hz, l66.3248 Hz, and 935.6743 Hz, respectively. The frequency sampling of l66.3248 Hz results showed the best accuracy performance of the ARIMAX model structure. The model order which is selected based on the minimization of the RMSE value obtained the best estimate, while the order models were selected based on the minimization of the AIC value showed a more consistent performance on the data estimation and validation.
AB - In the development of medical rehabilitation tools, muscle signals or electromyographic (EMG) are often used as feedback from the body in order to facilitate normal movement patterns. Through this signal, the force, torque, and angle can be estimated to produce the desired movement in the robot to assist patients in rehabilitation therapy. There had been various research done on estimating the extremities' joint angle, including the system identification method with Auto-Regressive Integrated Moving Average with Exogenous Input (ARIMAX) model structure. This research aims to model biceps brachii's EMG signal and the elbow joint angle using the ARIMAX model structure. In modeling, the order of the model was selected using the methods of minimizing the AIC (Akaike Information Criteria) and RMSE (Root Mean Square Error) values. The reported results showed the comparison of the outcome and model orders from three data sets with a frequency sampling of 47.6690 Hz, l66.3248 Hz, and 935.6743 Hz, respectively. The frequency sampling of l66.3248 Hz results showed the best accuracy performance of the ARIMAX model structure. The model order which is selected based on the minimization of the RMSE value obtained the best estimate, while the order models were selected based on the minimization of the AIC value showed a more consistent performance on the data estimation and validation.
UR - http://www.scopus.com/inward/record.url?scp=85139174720&partnerID=8YFLogxK
U2 - 10.1063/5.0108512
DO - 10.1063/5.0108512
M3 - Conference contribution
AN - SCOPUS:85139174720
T3 - AIP Conference Proceedings
BT - 3rd International Conference on Physical Instrumentation and Advanced Materials, ICPIAM 2021
A2 - Syarifah, Ratna Dewi
A2 - Sutisna, null
A2 - Maulina, Wenny
PB - American Institute of Physics Inc.
T2 - 3rd International Conference on Physical Instrumentation and Advanced Materials, ICPIAM 2021
Y2 - 27 October 2021
ER -