Modeling and control of an active knee orthosis using a computational model of the musculoskeletal system


One-third of the stroke survivors remain with some disability, needing assistance to perform the activities of daily life and therapy to recover the lost functions.  The robotic rehabilitation is a promissed field in this context improving the effectiveness of the treatment. Many researches have focused on developing human-robot interaction control to ensure user safety and therapy efficiency, but the validation of these controllers often requires contact between humans and robots, which involves cost, time and risk of accidents. This work aims to present a computational model of an ideal active orthosis used to assist the knee movement as a tool for test and validate human-robot interaction controls. Three controllers were applied to make the orthosis move the knee tracking the desired trajectory: a PID controller, an Inverse Dynamics-Based controller, and a Feedback-Feedforward Controller. The model proved to be useful and the controller with the best performance was the Feedback-Feedforward one.


ANDROWIS, G. J.; PIKAR, R.; RAMANUJAM, A.; NOLAN, K. J. Electromyography assessment during gait in a robotic exoskeleton for acute stroke. Frontiers in Neurology, v. 9, p. 630, 2018.

DELP, S. L.; ANDERSON, F. C.; ARNOLD, A. S.; LOAN, P.; HABIB, A.; JOHN, C. T.; GUENDELMAN, E.; THELEN, D. G. Opensim: Open-source software to create and analyze dynamic simulations of movement. IEEE Transactions on Biomedical Engineering, v. 54, n. 11, p. 1940–1950, 2007.

DIAZ, I.; GIL, J. J.; SANCHEZ, E. Lower-limb robotic rehabilitation: Literature review and challenges.
Journal of Robotics, v. 2011, 2011.

DURANDAU, G.; SARTORI, M.; BORTOLE, M.; MORENO, J. C.; PONS, J. L.; FARINA, D. Emg-driven models of human-machine interaction in individuals wearing the h2 exoskeleton. IFAC-PapersOnLine, v. 49, p. 200–203, 2016.

HOGAN, N. Impedance control: An approach to manipulation: Part 1-3. Journal of Dynamic Systems, Measurement, and Control, v. 107, p. 1–24, 1985.

HUANG, V. S.; KRAKAUER, J. W. Robotic neurorehabilitation: a computational motor learning perspective. Journal of NeuroEngineering and Rehabilitation, v. 6, n. 1, p. 5, Feb 2009. ISSN 1743-0003. Disponível em: .

IBARRA, J. C. P.; SIQUEIRA, A. A. G. Impedance control of rehabilitation robots for lower limbs, review.
In: 2014 Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol. [S.l.: s.n.], 2014. p. 235–240.

JUTINICO, A. L.; JAIMES, J. C.; ESCALANTE, F. M.; PEREZ-IBARRA, J. C.; TERRA, M. H.; SIQUEIRA, A. A. G. Impedance control for robotic rehabilitation: A robust markovian approach. Frontiers in Neurorobotics, v. 11, p. 43, 2017. ISSN 1662-5218. Disponível em: 10.3389/fnbot.2017.00043>.

KHAMAR, M.; EDRISI, M. Designing a backstepping sliding mode controller for an assistant human knee exoskeleton based on nonlinear disturbance observer. Mechatronics, v. 54, p. 121–132, 2018.

KIA, M.; STYLIANOU, A. P.; GUESS, T. M. Evaluation of a musculoskeletal model with prosthetic knee through six experimental gait trials. Medical Engineering & Physics, v. 36, 2014.

LLOYD, D. G.; BESIER, T. F. An emg-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. Journal of Biomechanics, v. 36, p. 765–776, 2003.

MACKAY, J.; MENSAH, G. A. The Atlas of Heart Disease and Stroke. [S.l.]: World Health Organization,

MJ, K.; ML, H. The effect of knee model on estimates of muscle and joint forces in recumbent pedaling.
ASME. J Biomech Eng., v. 132.

MOISSENET, F.; CHÈZE, L.; DUMAS, R. A 3d lower limb musculoskeletal model for simultaneous
estimation of musculo-tendon, joint contact, ligament and bone forces during gait. Journal of Biomechanics, v. 47, 2014.

NUNES, P. F.; SANTOS, W. M.; NOGUEIRA, S.; SIQUEIRA, A. A. G. Analyzing motor primitives of healthy subjects wearing a lower limb exoskeleton. In: International Congress of Mechanical Engineering – COBEM. [S.l.: s.n.], 2017. v. 1.

NUNES, P. F.; SANTOS, W. M. dos; SIQUEIRA, A. A. G. Control strategy based on kinetic motor
primitives for lower limbs exoskeletons. In: INTERNATIONAL FEDERATION OF AUTOMATIC
CONTROL. 10th IFAC Symposium on Biological and Medical Systems. [S.l.], 2018.

PANCHAL, N.; SANJEEVI, N. S. S.; VASHISTA, V. Lower limb musculoskeletal stiffness analysis during
swing phase as a cable-driven serial chain system. In: 2018 7th IEEE International Conference on
Biomedical Robotics and Biomechatronics (Biorob). [S.l.: s.n.], 2018.

PEÑA, G. G. Controle de Impedância Adaptativo Dirigido por EMG para Reabilitação Robótica. Tese
(phdthesis) — Universidade de São Paulo, São Carlos, 2017.

SETH, A.; PANDY, M. G. A neuromusculoskeletal tracking method for estimating individual muscle
forces in human movement. Journal of Biomechanics, v. 40, p. 326–366, 2007.

SHAO, Q.; BASSETT, D. N.; MANAL, K.; BUCHANAN, T. S. An emg-driven model to estimate muscle forces and joint moments in stroke patients. Computers in Biology and Medicine, v. 39, p. 1083–1088.

SOUSA, A. C. C. de; RAMOS, F. M.; DORADO, M. C. N.; FONSECA, L. O.; Bó, A. P. L. A comparative
study on control strategies for fes cycling using a detailed musculoskeletal model. IFAC-PapersOnline,
p. 204–209, 2016.

THELEN, D. G.; ANDERSON, F. C.; DELP, S. L. Generating dynamic simulations of movement using
computed muscle control. Journal of Biomechanics, v. 36, n. 3, p. 321–328, 2003.

WALTER, J. P.; PANDY, M. G. Dynamic simulation of knee-joint loading during gait using force-feedback
control and surrogate contact modelling. Medical Engineering & Physics, v. 48, p. 196–205, 2017.

WHO. World Report on Disability. 2011. Disponível em: 2011/report.pdf>.

WHO. The top 10 causes of death. 2018. Disponível em: detail/the-top-10-causes-of-death>.
How to Cite
MOSCONI, Denis; NUNES, Polyana Ferreira; SIQUEIRA, Adriano Almeida Gonçalves. Modeling and control of an active knee orthosis using a computational model of the musculoskeletal system. Journal of Mechatronics Engineering, [S.l.], v. 1, n. 3, p. 12 - 19, dec. 2018. ISSN 2595-3230. Available at: <>. Date accessed: 17 jan. 2019. doi: