Research Article

OpenSim-Based Coupled Lower Limb Rehabilitation Robots

Abstract

This study introduces a human-machine model utilizing OpenSim. It examines the impact of a passive (unactuated) lower-limb rehabilitation exoskeleton on biomechanics during ambulation. The model assesses how well the joints are aligned, how the muscles are used, and how well the design performs. The exoskeleton is made of T6061 aluminum alloy, which makes it light and easy to move. Each leg has three active joints and two passive joints that work in the sagittal plane. Musculoskeletal and exoskeleton models are simulated together in MATLAB and OpenSim. MATLAB scripts set their dynamic properties. A six-degree-of-freedom bushing models the interaction between the human and the exoskeleton at contact points. Joint angles come from experimental gait measurements. A residual-reduction algorithm reduces dynamic errors while keeping the resulting residual forces and moments within acceptable limits. Muscle activations and forces are estimated using computed muscle control, which follows joint paths. Simulations show that even in passive mode, the exoskeleton increases overall lower-limb muscle activation by more than 50% compared to walking without assistance. Significant increases occur in the rectus femoris, gluteus maximus, semimembranosus, and vastus lateralis. Joint torques also change: swing-leg hip and knee torques decrease by about 50%, support-leg torques increase because of the load, and ankle torque adjusts for compensation. These non-invasive simulations show reduced torque variability and support better design updates. This leads to improved exoskeleton alignment and evaluation before physical prototyping.

1. Banala SK, Kim SH, Agrawal SK, Scholz JP. Robot-assisted gait training with active leg exoskeleton (ALEX). IEEE Trans Neural Syst Rehabil Eng. 2008;17(1):2–8. doi:10.1109/TNSRE.2008.2008282.
2. Bortoletto R, Sartori M, He F, Pagello E. Modeling and simulating compliant movements in a musculoskeletal bipedal robot. In: Proceedings of the International Conference on Simulation, Modeling, and Programming for Autonomous Robots; 2012 Nov 5-8; Tsukuba, Japan.
3. Dollar AM, Herr H. Lower extremity exoskeletons and active orthoses: challenges and state-of-the-art. IEEE Trans Robot. 2008;24(1):144-158. doi:10.1109/TRO.2007.914781.
4. Donati M, Vitiello N, De Rossi SMM, et al. A flexible sensor technology for the distributed measurement of interaction pressure. Sensors (Basel). 2013;13(1):1021-1045. doi:10.3390/s130101021.
5. Ferrati F, Bortoletto R, Pagello E. Virtual modelling of a real exoskeleton constrained to a human musculoskeletal model. In: Proceedings of the Conference on Biomimetic and Biohybrid Systems; 2013 Jul 29-Aug 1; London, UK.
6. Grey JE, Enoch S, Harding KG. ABC of wound healing: venous and arterial leg ulcers. BMJ. 2006;332(Suppl S4). doi:10.1136/bmj.332.7537.S2.
7. Kawamoto H, Hayashi T, Sakurai T, Eguchi K, Sankai Y. Development of a single leg version of HAL for hemiplegia. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2009 Sept 3-6; Minneapolis, MN.
8. Li LJ, Zhang YX. Biomechanical simulation of Achilles tendon strains during hurdling. Adv Mater Res. 2013;647:462-465. doi:10.4028
9. Manns P, Sreenivasa M, Millard M, Mombaur K. Motion optimization and parameter identification for a human and lower back exoskeleton model. IEEE Robot Autom Lett. 2017;2(3):1564-1570. doi:10.1109/LRA.2017.2678567.
10. Reinbolt JA, Seth A, Delp SL. Simulation of human movement: applications using OpenSim. Procedia IUTAM. 2011;2:186-198. doi:10.1016/j.piutam.2011.04.018.
11. Rodríguez-Fernández A, Lobo-Prat J, Font-Llagunes JM. Systematic review on wearable lower-limb exoskeletons for gait training in neuromuscular impairments. J Neuroeng Rehabil. 2021;18(1):22. doi:10.1186/s12984-021-00813-2.
12. Strausser KA, Kazerooni H. The development and testing of a human-machine interface for a mobile medical exoskeleton. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems; 2011 Sept 25-30; San Francisco, CA.
13. Uchida TK, Hicks JL, Dembia CL, Delp SL. Stretching your energetic budget: how tendon compliance affects the metabolic cost of running. PLoS One. 2016;11(3):e0150378. doi:10.1371/journal.pone.0150378.
14. Vallery H, Van Asseldonk EH, Buss M, Van Der Kooij H. Reference trajectory generation for rehabilitation robots: complementary limb motion estimation. IEEE Trans Neural Syst Rehabil Eng. 2008;17(1):23–30. doi:10.1109/TNSRE.2008.2008280.
15. Veneman JF, Kruidhof R, Hekman EE, et al. Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2007;15(3):379–386. doi:10.1109/TNSRE.2007.903919.
16. Zhang J, Fiers P, Witte KA, et al. Human-in-the-loop optimization of exoskeleton assistance during walking. Science. 2017;356(6344):1280-1284. doi:10.1126/science.aah7654.
17. Zhao C, Liu Z, Ou Y, Zhu L. Mechanical structure design and motion simulation analysis of a lower limb exoskeleton rehabilitation robot based on human-machine integration. Sensors (Basel). 2025;25(5):1611. doi:10.3390/s25051611.
18. Su D, Hu H, Wu X, Shang J, Luo H. Review of adaptive control for stroke lower-limb exoskeleton rehabilitation robots based on motion-intention recognition. Front Neurorobotics. 2023;17:1186175. doi:10.3389/fnbot. 2023.1186175.
19. Zhang Y, Zhao W, Wan C, et al. Exoskeleton rehabilitation robot training for balance and lower limb function in sub-acute stroke patients: a pilot, randomized controlled trial. J Neuroeng Rehabil. 2024;21:98. doi:10.1186/s12984-024-01391-0.
20. Zhu Z. Design and motion control of exoskeleton robot for lower limb rehabilitation. Frontiers in Neuroscience. 2024;18:1355052. doi:10.3389/fnins.2024.1355052.
IssueArticles in Press QRcode
SectionResearch Article(s)
Keywords
Exoskeleton device; Musculoskeletal system, gait; Biomechanical phenomena; Muscle activation; Joint torque; Simulation models; Passive assistance

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How to Cite
1.
Khalil W. OpenSim-Based Coupled Lower Limb Rehabilitation Robots. jmr. 2025;(-).