Design and Motion Control of a 2-DoF Exoskeleton Robot for Ankle Joint Rehabilitation of Post-Stroke Patients
Received: 6 May 2025 | Revised: 21 June 2025 | Accepted: 3 July 2025 | Online: 6 October 2025
Corresponding author: Eko Wahyu Abryandoko
Abstract
Post-stroke patients often experience motor function impairments in the lower extremities, including the ankle joint, which can hinder walking ability and body balance. Rehabilitation plays a crucial role in restoring these functions; however, it typically requires a lengthy process and intensive involvement from medical professionals. This study aims to design and develop a Two-Degree-of-Freedom (2-DoF) ankle exoskeleton system, incorporating dorsiflexion–plantarflexion and inversion–eversion movements, as an alternative automated and programmable rehabilitation therapy. The system's performance was evaluated by comparing the actual joint movement angles with reference angles set through preset inputs. Tests were conducted under both unloaded conditions and loaded conditions simulating the limb mass of users. The results showed that for dorsiflexion–plantarflexion movements, the average angle error was 1.9° under unloaded conditions, increasing to 2.7° when a load was applied. For inversion–eversion movements, the average error was 2.4° without load and 3.2° with load. The system demonstrated operational stability and consistency in following the programmed motion trajectories. Based on these findings, the developed exoskeleton has potential as an effective, safe, and efficient rehabilitation therapy device to support functional recovery in post-stroke patients.
Keywords:
exoskeleton, post-stroke rehabilitation, ankle joint, angle trackingDownloads
References
X. Zhang, Z. Yue, and J. Wang, "Robotics in Lower-Limb Rehabilitation after Stroke," Behavioural Neurology, vol. 2017, no. 1, June 2017, Art. no. 3731802.
K. I. Paraskevas, "Prevention and treatment of strokes associated with carotid artery stenosis: a research priority," Annals of Translational Medicine, vol. 8, no. 19, pp. 1260–1260, Oct. 2020.
M. da Miao, X. shan Gao, J. Zhao, and P. Zhao, "Rehabilitation robot following motion control algorithm based on human behavior intention," Applied Intelligence, vol. 53, no. 6, pp. 6324–6343, Mar. 2023.
K. Bamforth, P. Rae, J. Maben, H. Lloyd, and S. Pearce, "Perceptions of healthcare professionals’ psychological wellbeing at work and the link to patients’ experiences of care: A scoping review," International Journal of Nursing Studies Advances, vol. 5, Dec. 2023, Art. no. 100148.
C. J. Winstein et al., "Guidelines for Adult Stroke Rehabilitation and Recovery," Stroke, vol. 47, no. 6, pp. e98–e169, June 2016.
D. M. G. Preethichandra et al., "Passive and Active Exoskeleton Solutions: Sensors, Actuators, Applications, and Recent Trends," Sensors, vol. 24, no. 21, Nov. 2024, Art. no. 7095.
R. K. Saleh, W. S. Aboud, and S. M. Haris, "A Review Study for Robotic Exoskeletons Rehabilitation Devices," Al-Nahrain Journal for Engineering Sciences, vol. 26, no. 6, pp. 63–73, July 2023.
S. Z. Ying, N. K. Al-Shammari, A. A. Faudzi, and Y. Sabzehmeidani, "Continuous Progressive Actuator Robot for Hand Rehabilitation," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5276–5280, Feb. 2020.
Y. Xing and C. Lv, "Dynamic State Estimation for the Advanced Brake System of Electric Vehicles by Using Deep Recurrent Neural Networks," IEEE Transactions on Industrial Electronics, vol. 67, no. 11, pp. 9536–9547, Nov. 2020.
L. Huang, J. Zheng, Y. Gao, Q. Song, and Y. Liu, "A Lower Limb Exoskeleton Adaptive Control Method Based on Model-free Reinforcement Learning and Improved Dynamic Movement Primitives," Journal of Intelligent & Robotic Systems, vol. 111, no. 1, Feb. 2025, Art. no. 24.
X. Zhang, H. Li, Z. Lu, and G. Yin, "Homology Characteristics of EEG and EMG for Lower Limb Voluntary Movement Intention," Frontiers in Neurorobotics, vol. 15, June 2021, Art. no. 642607.
S. Hassani and U. Dackermann, "A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring," Sensors, vol. 23, no. 4, Feb. 2023, Art. no. 2204.
Z. Chen, Q. Guo, H. Xiong, D. Jiang, and Y. Yan, "Control and Implementation of 2-DOF Lower Limb Exoskeleton Experiment Platform," Chinese Journal of Mechanical Engineering, vol. 34, no. 1, Feb. 2021, Art. no. 22.
S. Kim and S. Kwon, "Robust transition control of underactuated two-wheeled self-balancing vehicle with semi-online dynamic trajectory planning," Mechatronics, vol. 68, June 2020, Art. no. 102366.
D. P. Losey and M. K. O’Malley, "Trajectory Deformations From Physical Human–Robot Interaction," IEEE Transactions on Robotics, vol. 34, no. 1, pp. 126–138, Feb. 2018.
C. L. Brockett and G. J. Chapman, "Biomechanics of the ankle," Orthopaedics and Trauma, vol. 30, no. 3, pp. 232–238, June 2016.
R. Baud, A. R. Manzoori, A. Ijspeert, and M. Bouri, "Review of control strategies for lower-limb exoskeletons to assist gait," Journal of NeuroEngineering and Rehabilitation, vol. 18, no. 1, July 2021, Art. no. 119.
S. Ibaraki and R. Saito, "Novel kinematic model of articulated arm coordinate measuring machine with angular position measurement errors of rotary axes," CIRP Annals, vol. 72, no. 1, pp. 449–452, Jan. 2023.
S. Bruno, M. José, S. Filomena, C. Vítor, M. Demétrio, and B. Karolina, "The Conceptual Design of a Mechatronic System to Handle Bedridden Elderly Individuals," Sensors, vol. 16, no. 5, May 2016, Art. no. 725.
E. W. Abryandoko, S. Susmartini, P. W. Laksono, and L. Herdiman, "Simulation and Modeling of Hybrid Assistive Robotic Neuromuscular Dynamic Stimulation for Upper Limb Rehabilitation," Journal of Applied Science and Engineering, vol. 28, no. 5, pp. 925–933, July 2024.
H. S. Choi, C. H. Lee, and Y. S. Baek, "Design and Validation of a Two-Degree-of-Freedom Powered Ankle-Foot Orthosis with Two Pneumatic Artificial Muscles," Mechatronics, vol. 72, Dec. 2020, Art. no. 102469.
T. Lee, I. Kim, and Y. S. Baek, "Design of a 2DoF Ankle Exoskeleton with a Polycentric Structure and a Bi-Directional Tendon-Driven Actuator Controlled Using a PID Neural Network," Actuators, vol. 10, no. 1, Jan. 2021, Art. no. 9.
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