Online Usage of Biomechanical and Simulation Software in Analysis of the Performances of Rehabilitation Robots, Using Simulation Technique

Document Type : Original Articles


1 PhD in Control Field, Department of Electrical Engineering, School of Engineering, University of Isfahan, Isfahan, Iran

2 Assistant Professor, Department of Electrical Engineering, School of Engineering, University of Isfahan, Isfahan, Iran

3 Associate Professor, Department of Orthoses and prostheses, School of Rehabilitation Sciences, Isfahan University of Medical Sciences, Isfahan, Iran



Introduction: Rehabilitation robots have the ability to assist the patients with paralysis and semi-paralysis. Besides, these robots are capable of being programmed to perform various rehabilitation methods. However, evaluating their functions and their effects on human’s body are still two of the main challenges of theses robots. The purpose of the present study was to introduce a method for assessing the function of a rehabilitation robot in modifying the crouch gait to normal gait, by using online biomechanics and computational software.Materials and Methods: Rehabilitation robot and human leg were simulated using Inventor (Autodesk, Inc.) and OpenSim (Stanford University) software. User’s muscle strength was calculated according to a crouch gait. The system got the position of each joint and muscle strength as input, and determined the torque required for each hip and knee joints.Results: The performance of rehabilitation robot on human body was evaluated by relating the simulation in biomechanical and computational software. The kinematic and kinetic effects of robots on model of human model with crouch gait pattern was confirmed. In addition, the error of tracking normal gait with wearable robot was less than 0.06 rad for user with crouch gait.Conclusion: By using a simulation method and analyzing the motion data of a person gait pattern, an optimal path can be defined individually for each person, which reduces the risk and error of tracking while using the rehabilitation robot. It is also possible to change the mechanical and control structure of wearable robots in simulation without the cost and risk of laboratory evaluation.


  1. Sokhangouei Y, Abdollahi I, Kazem-Dokht M, Karimlou M, Khanlari Z. Measuring the quadriceps angle by a new method and comparison with goniometer and radiography. J Rehab 2012; 13(2):64-73. [In Persian].
  2. Meuleman J, van Asseldonk E, van Oort G, Rietman H, van der Kooij H. LOPES II--Design and evaluation of an admittance controlled gait training robot with shadow-leg approach. IEEE Trans Neural Syst Rehabil Eng 2016; 24(3): 352-63.
  3. Gams A, Petric T, Debevec T, Babic J. Effects of robotic knee exoskeleton on human energy expenditure. IEEE Trans Biomed Eng 2013; 60(6): 1636-44.
  4. Young AJ, Hargrove LJ. A Classification method for user-independent intent recognition for transfemoral amputees using powered lower limb prostheses. IEEE Trans Neural Syst Rehabil Eng 2016; 24(2): 217-25.
  5. Herr H. Exoskeletons and orthoses: Classification, design challenges and future directions. J Neuroeng Rehabil 2009; 6: 21.
  6. Cao J, Xie SQ, Das R, Zhu GL. Control strategies for effective robot assisted gait rehabilitation: the state of art and future prospects. Med Eng Phys 2014; 36(12): 1555-66.
  7. Ong CF, Hicks JL, Delp SL. Simulation-based design for wearable robotic systems: an optimization framework for enhancing a standing long jump. IEEE Trans Biomed Eng 2016; 63(5): 894-903.
  8. Afschrift M, De GF, De SJ, Jonkers I. The effect of muscle weakness on the capability gap during gross motor function: A simulation study supporting design criteria for exoskeletons of the lower limb. Biomed Eng Online 2014; 13: 111.
  9. Chao EY, Armiger RS, Yoshida H, Lim J, Haraguchi N. Virtual Interactive Musculoskeletal System (VIMS) in orthopaedic research, education and clinical patient care. J Orthop Surg Res 2007; 2: 2.
  10. Agarwal P, Kuo PH, Neptune RR, Deshpande AD. A novel framework for virtual prototyping of rehabilitation exoskeletons. IEEE Int Conf Rehabil Robot 2013; 2013: 6650382.
  11. Ferrati F, Bortoletto R, Pagello E. Virtual modelling of a real exoskeleton constrained to a human musculoskeletal model. Proceedings of the Biomimetic and Biohybrid Systems- 2nd International Conference, Living Machines; 2013 29 Jul-2 Aug; London, UK.
  12. Agarwal P, Sathia narayanan M, Lee L, Mendel F, Krovi V. Simulation-based design of exoskeletons using musculoskeletal analysis. Proceedings of the IDETC 2010: ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference; 2010 15-18 Aug; Montreal, Quebec, Canada.
  13. Viteckova S, Kutilek P, Jirina M. Wearable lower limb robotics: A review. Biocybern Biomed Eng 2013; 33(2): 96-105.
  14. Delp SL, Anderson FC, Arnold AS, Loan P, Habib A, John CT, et al. OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng 2007; 54(11): 1940-50.
  15. Karimi MT, Kaviani Boroojeni MT. The analysis of the length and produced force by some trunk muscles of a scoliotic patient using Open-SIMM software during walking with Milwaukee orthosis-A case report. J Res Rehabil Sci 2013; 9(7): 1344-1352. [In Persian].
  16. Mansouri M, Reinbolt JA. A platform for dynamic simulation and control of movement based on OpenSim and MATLAB. J Biomech 2012; 45(8): 1517-21.
  17. Stanev D. Extendable OpenSim-Matlab Infrastructure using class oriented C++ Mex Interface. Bethesda, MD: The National Institutes of Health (NIH); 2016.
  18. Chen B, Ma H, Qin LY, Gao F, Chan KM, Law SW, et al. Recent developments and challenges of lower extremity exoskeletons. J Orthop Translat 2016; 5: 26-37.
  19. Sharifmoradi K, Karimi M T, Rezaeeyan Z. The effects of negative heel rocker shoes on the moment and the contact forces applied on lower limb joints of diabetic patients during walking. Physical Treatment 2016; 6(3): 129-36.
  20. Khamar M, Edrisi M, Zahiri M. Human-exoskeleton control simulation, kinetic and kinematic modeling and parameters extraction. MethodsX 2019; 6: 1838-46.
  21. Khamar M, Edrisi M. Designing a backstepping sliding mode controller for an assistant human knee exoskeleton based on nonlinear disturbance observer. Mechatronics 2018; 54: 121-32.