Post-Stroke Rehabilitation Exosceleton Movement Control using EMG Signal

Akif Rahmatillah, Osmalina Nur Rahma, Muhammad Amin, Septian Indra Wicaksana, Khusnul Ain, Riries Rulaningtyas


Post-stroke rehabilitation device is very important nowadays, considering the high rate of disability caused by stroke especially arm function. About 50% of stroke survivors experience the unilateral motor deficits which decreased upper extremity function. Therefore, hand and shoulders therapy are generally performed in advance to support patients’ daily activities. Electromyograph (EMG) signals from selective muscles were proven to provide additional power for post-stroke rehabilitation device to recover more quickly because the patient participates actively in rehabilitation. This paper describes a preliminary prototype of upper limb exoskeleton for post-stroke therapy devices utilizes automatic control algorithm to control human arm movement with one degree of freedom based on a myoelectric signal of muscle biceps brachii from their unaffected side. This study used low-cost instruments and digital signal processing, such as IIR low pass filter followed by Kalman filter to generate the myoelectric signal that separated from noise as an input for controlling the DC motor which moved the exoskeleton of arm therapy mechanic. The accuracy of system performance  in this study was 95%. Hopefully, this device can help stroke survivors to perform therapy independently without depending on therapists so that rehabilitation will be more effective and efficient.


Post-stroke rehabilitation; EMG; Exoskeleton; IIR Filter; Kalman Filter

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Ministry of Health, “Presiden Resmikan RS Pusat Otak Nasional,†2014. [Online]. Available: [Accessed: 21-Feb-2018].

J. A. Franck, “Concise Arm and Hand Rehabilitation Approach in Stroke,†vol. 3, no. 4, 2015.

R. Song, K. Tong, X. Hu, and L. Li, “Assistive Control System Using Continuous Myoelectric Signal in Robot-Aided Arm Training for Patients After Stroke,†IEEE Trans. Neural Syst. Rehabil. Eng. (Volume16 , Issue 4 ), vol. 16, no. 4, pp. 371–379, 2008.

W. H. Chang and Y.-H. Kim, “Robot-assisted Therapy in Stroke Rehabilitation,†J. Stroke, vol. 15, no. 3, p. 174, 2013.

C. G. Burgar, P. S. Lum, P. C. Shor, and H. F. Machiel Van der Loos, “Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience.,†J. Rehabil. Res. Dev., vol. 37, no. 6, pp. 663–73, 2000.

M. Reinvee, P. Vaas, J. Ereline, and M. Pääsuke, “Applicability of Affordable sEMG in Ergonomics Practice,†Procedia Manuf., vol. 3, no. Ahfe, pp. 4260–4265, 2015.

Olimex, “Sheild EKG-EMG - Open Source Hardware Board,†no. June, 2015.

T. Triwiyanto, O. Wahyunggoro, H. A. Nugroho, and H. Herianto, “Evaluating the performance of Kalman filter on elbow joint angle prediction based on electromyography,†Int. J. Precis. Eng. Manuf., vol. 18, no. 12, pp. 1739–1748, 2017.

M. Grewal and A. Andrews, Kalman filtering: theory and practice using MATLAB, vol. 5. 2011.

G. Welch and G. Bishop, “An Introduction to the Kalman Filter,†In Pract., vol. 7, no. 1, pp. 1–16, 2006.



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