Controlling an Upper-Limb Rehabilitation Robot by EMG Signals
VERSION OF RECORD ONLINE: 17/09/2025
Corressponding author's email:
thientd@hcmute.edu.vnDOI:
https://doi.org/10.54644/jte.2025.1783Keywords:
Rehabilitation robot, Upper limb exoskeleton, EMGs, Kalman filter, JacobianAbstract
This paper presents a method for controlling the motion of a 6-DOF upper limb rehabilitation robot based on electromyography (EMG) signal. A kinematic model has been determined by Denavit - Hartenberg (D-H) method. Due to the complexity of the robot mechanical structure, the inverse kinematics is addressed using the Jacobian method. Besides, the EMG signal is a physiological signal generated during muscle contraction, which is collected from a low-cost sensor interacted on the human arm, but noise is inevitable in the extracted data. Therefore, the method treats EMG signal noise using Butterworth filter and proposes a method to predict elbow joint angle based on EMG using zero crossing and Kalman filter. Finally, the proposed method is evaluated through simulation on MATLAB Simulink and experiments on a 6-DOF robot arm model. The experimental results show that the EMG signal processing method proposed is significantly effective and the upper limb rehabilitation robot based on EMG signals is feasible.
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References
“Statistics on Stroke in Vietnam,” (in Vietnamese), 2022. [Online]. Available: https://tinyurl.com/29k7g6dr
Viet Nam Ministry of Health, “Shortage of rehabilitation human resources in Vietnam,” (in Vietnamese), Viet Nam Ministry of Health Portal, 2023.
L. Marchal-Crespo and R. Riener, “The effectiveness of robotic training depends on motor task characteristics,” Exp. Brain Res., vol. 235, no. 5, pp. 1307–1315, 2017, doi: 10.1007/s00221-017-5099-9.
J. C. Perry and J. Rosen, “Isotropy of an upper limb exoskeleton and the kinematics and dynamics of the human arm,” Appl. Bionics Biomech., vol. 6, no. 2, pp. 175–191, 2009, doi: 10.1080/11762320902920575.
M. Dežman, A. Ude, and A. Gams, “Mechanical design and friction modelling of a cable-driven upper-limb exoskeleton,” Mech. Mach. Theory, vol. 170, p. 104701, 2022.
N. N. Thanh, T. P. Manh, N. N. Minh, T. T. Quoc, and T. D. Thien, “Modeling, kinematic analysis, and simulation for a 6-DOF upper limb rehabilitation robot,” in Computational Intelligence Methods for Green Technology and Sustainable Development, Cham, Switzerland: Springer, 2024, pp. 12–24.
M. A. Gull, S. H. Bengtson, M. Mohammadi, L. N. S. A. Struijk, T. B. Moeslund, T. Bak, and S. Bai, “A 4-DOF upper limb exoskeleton for physical assistance: Design, modeling, control and performance evaluation,” Appl. Sci., vol. 11, no. 15, p. 7139, 2021.
L. Hang, R. Ren, and J. Wang, “Design and analysis of an upper limb rehabilitation robot based on multimodal control,” Sensors, vol. 23, no. 21, p. 8801, 2023, doi: 10.3390/s23218801.
S. Pei, J. Guo, H. Yin, and Y. Yao, “A human-like inverse kinematics algorithm of an upper limb rehabilitation exoskeleton,” Symmetry, vol. 15, no. 9, p. 1657, 2023, doi: 10.3390/sym15091657.
D. T. Tran, T. D. Nguyen, M. K. Tran, and K. K. Ahn, “Development of a hardware-in-the-loop platform for a teleoperation flexibility robotic system,” Appl. Sci., vol. 14, no. 5, p. 2207, 2024, doi: 10.3390/app14052207.
A. M. Simon, L. J. Hargrove, B. A. Lock, and T. A. Kuiken, “Target achievement control test: Evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses,” J. Rehabil. Res. Dev., vol. 48, no. 6, pp. 619–628, 2011.
Y. Tao, S. Guo, Y. Li, X. Li, and F. Xi, “Review of sEMG for robot control: Techniques and applications,” Appl. Sci., vol. 13, no. 17, p. 9546, 2023, doi: 10.3390/app13179546.
Y. Huo, W. Zhao, H. Hu, and L. Li, “Effects of EMG-based robot for upper extremity rehabilitation on post-stroke patients: A systematic review and meta-analysis,” Front. Physiol., vol. 14, p. 1172958, 2023, doi: 10.3389/fphys.2023.1172958.
S. Bisi, B. Shrestha, Z. Yang, and V. Gandhi, “Development of an EMG-controlled mobile robot,” Robotics, vol. 7, no. 3, p. 36, 2018, doi: 10.3390/robotics7030036.
M. Pang, Q. Huang, H. Ishihara, and H. Hirata, “Electromyography-based quantitative representation method for upper-limb elbow joint angle in sagittal plane,” J. Med. Biol. Eng., vol. 35, no. 3, pp. 356–364, 2015.
T. Tuncer and A. Subasi, “Surface EMG signal classification using ternary pattern and discrete wavelet transform-based feature extraction for hand movement recognition,” Biomed. Signal Process. Control, vol. 58, p. 101872, 2020, doi: 10.1016/j.bspc.2020.101872.
G. Naik, Biomedical Signal Processing: Advances in Theory, Algorithms and Applications. Cham, Switzerland: Springer, 2019.
M. S. H. Reaz, F. M. Y. Mohamed, and M. I. B. Hussain, “Techniques of EMG signal analysis: Detection, processing, classification and applications,” Biol. Proced. Online, vol. 8, pp. 11–35, 2006, doi: 10.1251/bpo115.
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