TY - GEN
T1 - Development of Elbow Joint Exoskeleton Control System Using Fuzzy Pid Control Method for Post Stroke Rehabilitation
AU - Sidabalok, Sintong Mangaraja
AU - Arifin, Achmad
AU - Pramudijanto, Josaphat
AU - Kusuma, Hendra
AU - Risciwan, Andra
AU - Zazuli, Moh Ismarintan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Spasticity refers to a post-stroke condition characterized by muscle stiffness, which occurs when patients are assisted in stretching their upper extremity muscles, muscle contractions appear against the direction of the passive movement being carried out. One of the rehabilitation technologies for stroke patients is the exoskeleton to present the intensity of therapy repetitions. An exoskeleton with PID control is not good at determining the output according to the rehabilitation procedure, so an additional control method is formed in the form of Fuzzy which determines the exoskeleton's movement target taking input from the torque obtained by the load cell pressure sensor and the position of the encoder. Normal subjects were used for the experiment, also given scenarios resembling the characteristics of stroke patients. The Proportional trials carried out using the KP position parameter in sinusoidal motion, KP 1000 is the ideal KP for a motor with an RMSE result of 0.3 degrees with on a smooth motion, as needed in a rehabilitation. Based on the collected sample data, the average fuzzy accuracy to determinate the suitable output stands at 98.12 percent across all scenarios. Based on this research, enhancement can be achieved by customizing the ideal KP for each subject, considering variations in their height and weight, and adjusting the KP value accordingly, and the resulting trajectory has an error minimum. And testing with stroke patients directly could be the next development.
AB - Spasticity refers to a post-stroke condition characterized by muscle stiffness, which occurs when patients are assisted in stretching their upper extremity muscles, muscle contractions appear against the direction of the passive movement being carried out. One of the rehabilitation technologies for stroke patients is the exoskeleton to present the intensity of therapy repetitions. An exoskeleton with PID control is not good at determining the output according to the rehabilitation procedure, so an additional control method is formed in the form of Fuzzy which determines the exoskeleton's movement target taking input from the torque obtained by the load cell pressure sensor and the position of the encoder. Normal subjects were used for the experiment, also given scenarios resembling the characteristics of stroke patients. The Proportional trials carried out using the KP position parameter in sinusoidal motion, KP 1000 is the ideal KP for a motor with an RMSE result of 0.3 degrees with on a smooth motion, as needed in a rehabilitation. Based on the collected sample data, the average fuzzy accuracy to determinate the suitable output stands at 98.12 percent across all scenarios. Based on this research, enhancement can be achieved by customizing the ideal KP for each subject, considering variations in their height and weight, and adjusting the KP value accordingly, and the resulting trajectory has an error minimum. And testing with stroke patients directly could be the next development.
KW - Exoskeleton
KW - Fuzzy-PID Controller
KW - Spasticity
KW - Stroke
KW - Voluntary Movement
UR - http://www.scopus.com/inward/record.url?scp=85171147771&partnerID=8YFLogxK
U2 - 10.1109/ISITIA59021.2023.10221019
DO - 10.1109/ISITIA59021.2023.10221019
M3 - Conference contribution
AN - SCOPUS:85171147771
T3 - 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding
SP - 310
EP - 315
BT - 2023 International Seminar on Intelligent Technology and Its Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023
Y2 - 26 July 2023 through 27 July 2023
ER -
