Design and Evaluation of a Multi-Mode Robotic Arm Orthosis using Musculoskeletal Simulation
Year 2018,
Volume: 33 Issue: 3, 133 - 144, 30.09.2018
Erkan Ödemiş
Cabbar Veysel Baysal
Abstract
Robotic upper extremity orthoses have been used in rehabilitation for therapy of neuromuscular disorders and successful implementations are demonstrated by numerous clinical results. Majority of researchers focused on orthotic devices enabling basic therapy mode operations. However, there is still need for new orthotic designs which facilitates therapy modes and assistance for daily life activities in coherence. In this work, design of a multi-mode two DoF robotic arm orthosis is introduced. The designed robotic orthosis is implemented in simulation and tested with a human arm musculoskeletal model, for compliant operation. It uses model based computed torque controller and is tested for multi-mode operation. The performance is evaluated for compliant operation of “Assistive” and “Resistive” rehabilitation modes. Performance tests yielded encouraging results for future developments.
References
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Robot-aided Sensorimotor Training in Stroke
Rehabilitation a Realistic Option? Curr. Opin.
Neurol. 14, 745–752.
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Majmundar, M., Van der Loos, M., 2002.
Robot-assisted Movement Training Compared
with Conventional Therapy Techniques for the
Rehabilitation of Upper-limb Motor Function
After Stroke. Arch. Phys. Med. Rehabil. 83,
952–959.
- 3. Gopura, R.A.R.C., Kiguchi, K., 2009.
Mechanical Designs of Active Upper-limb
Exoskeleton Robots State-of-the-art and
Design Difficulties. 2009 IEEE Int. Conf.
Rehabil. Robot. ICORR 2009 178–187
doi:10.1109/ICORR.2009.5209630.
- 4. Gopura, R.A.R.C., Bandara, D.S.V., Kiguchi,
K., Mann, G.K.I. 2016. Developments in
Hardware Systems of Active Upper-limb
Exoskeleton Robots: A review. Rob. Auton.
Syst. 75, 203–220
- 5. Marchal-Crespo, L., Reinkensmeyer, D.J.,
2009. Review of Control Strategies for Robotic
Movement Training After Neurologic Injury. J.
Neuroeng. Rehabil. 6, 20.
- 6. Wolbrecht, E.T., Chan, V., Reinkensmeyer, D.
J., Bobrow, J.E. 2008. Optimizing Compliant,
Model-based Robotic Assistance to Promote
Neurorehabilitation. IEEE Trans. Neural Syst.
Rehabil. Eng. 16, 286–297.
- 7. Lo, H.S., Xie, S.Q., 2012. Exoskeleton Robots
for Upper-limb Rehabilitation: State of the Art
and Future Prospects. Med. Eng. Phys. 34,
261–268.
- 8. Anam, K., Al-Jumaily, A.A., 2012. Active
Exoskeleton Control Systems: State of the Art.
Procedia Eng. 41, 988–994.
- 9. Lee, H.D., Lee, B.K., Kim, W.S., Han, J.S.,
Shin, K.S., Han, C.S., 2014. Human-robot
Cooperation Control Based on a Dynamic
Model of an Upper Limb Exoskeleton for
Human Power Amplification. Mechatronics 24,
168–176.
- 10. Rosen, J., Brand, M., Fuchs, M.B., Arcan, M.,
2001. A Myosignal-based Powered
Exoskeleton System. IEEE Trans. Syst. Man,
Cybern. Part ASystems Humans. 31, 210–222.
- 11. Sugar, T.G., He, J., Koeneman, E.J.,
Koeneman, J.B., Herman, R., Huang, H.,
Schultz, R.S., Herring, D.E., Wanberg, J.,
Balasubramanian, S., Swenson, P., Ward, J.A.,
2007. Design and Control of RUPERT: A
Device for Robotic Upper Extremity Repetitive
Therapy. IEEE Trans. Neural Syst. Rehabil.
Eng. 15, 336–346.
- 12. Gopura, R.A.R.C., Kiguchi, K., Yi, Y., 2009.
SUEFUL-7: A 7DOF Upper-limb Exoskeleton
Robot with Muscle-model-oriented EMGBased
Control. 2009 IEEE/RSJ Int. Conf.
Intell. Robot. Syst. IROS 2009 1126–1131,
doi:10.1109/IROS.2009.5353935
- 13. Crema, A., Mancuso, M., Frisoli, A., Selsedo
F., Raschella, F., Micea, S., 2015. A Hybrid
NMES-exoskeleton for Real Objects
Interaction. Int. IEEE/EMBS Conf. Neural
Eng. NER 2015–July, 663–666.
- 14. Carignan, C., Tang, J., Roderick, S., 2009.
Development of an Exoskeleton Haptic
Interface for Virtual Task Training. 2009
IEEE/RSJ Int. Conf. Intell. Robot. Syst. IROS
2009 3697–3702, doi:10.1109/IROS.2009.
5354834
- 15. Davoodi, R., Loeb, G. E., 2011. MSMS
Software for VR Simulations of Neural
Prostheses and Patient Training and
Rehabilitation. Stud. Health Technol. Inform.
163, 156–162.
- 16. Moromizato, K., Kimura, R., Fukase, H.,
Yamaguchi, K., Ishida, H., 2016. Whole-body
Patterns of the Range of Joint Motion in Young Adults: Masculine Type and Feminine Type. J.
Physiol. Anthropol. 35, 23.
- 17. Lewis F.L., Munro N., 2004. Robot
Manipulator Control Theory and Practice.
Marcel Dekker, Inc.
- 18. Delp, S. L., Loan, J. P., Hoy, M. G., Zajac, F.
E., Topp, E.L., Rosen, J.M., 1990. An
Interactive Graphics-based Model of the Lower
Extremity to Study Orthopedic Surgical
Procedures. IEEE Trans. Biomed. Eng. 37,
757–767.
- 19. Delp, S.L., Anderson, F.C., Arnold, A.S.,
Loan, P., Habib, A., John, C.T., Guendelman,
E., Thelen, D.G., 2007. OpenSim: Open Source
to Create and Analyze Dynamic Simulations of
Movement. IEEE Trans. Biomed. Eng. 54,
1940–1950.
- 20. Damsgaard, M., Rasmussen, J., Christensen,
S.T., Surma, E., de Zee, M., 2006. Analysis of
Musculoskeletal Systems in the AnyBody
Modeling System. Simul. Model. Pract. Theory
14, 1100–1111.
- 21. Cheng, E.J., Brown, I.E., Loeb, G.E., 2001.
Virtual Muscle: A Computational Approach to
Understanding the Effects of Muscle Properties
on Motor Control. J. Neurosci. Methods 106,
111–112.
- 22. Rosen, J., Fuchs, M. B., Arcan, M., 1999.
Performances of Hill-Type and Neural
Network Muscle Models-Toward a Myosignal-
Based Exoskeleton. Comput. Biomed. Res. 32,
415–439.
- 23. Zajac, F.E., 1989. Muscle and Tendon:
Properties, Models, Scaling, and Application to
Biomechanics and Motor Control. Crit. Rev.
Biomed. Eng. 17, 359–411.
- 24. Winters, J. M., 1995. An Improved Muscle-
Reflex Actuator for use in Large-scale
Neuromusculoskeletal Models. Ann. Biomed.
Eng. 23, 359–374.
- 25. Garner, B.A., Pandy, M.G., 2001.
Musculoskeletal Model of the Upper Limb
Based on the Visible Human Male Dataset.
Comput. Methods Biomech. Biomed. Engin. 4,
93–126.
- 26. Langenderfer, J., Jerabek, S.A., Thangamani,
V.B., Kuhn, J.E., Hughes, R.E., 2004.
Musculoskeletal Parameters of Muscles
Crossing the Shoulder and Elbow and the
Effect of Sarcomere Length Sample Size on
Estimation of Optimal Muscle Length. Clin.
Biomech. 19, 664–670.
- 27. Holzbaur, K.R.S., Murray, W.M., Delp, S.L.,
2005. A Model of the Upper Extremity for
Simulating Musculoskeletal Surgery and
Analyzing Neuromuscular Control. Ann.
Biomed. Eng. 33, 829–840.
- 28. Saul, K.R., Hu X., Goehler C.M., Vidt M.E.,
Daly M., Velisar A., Murray W.M., 2014.
Benchmarking of Dynamic Simulation
Predictions in Two Software Platforms using
an Upper Limb Musculoskeletal Model.
Comput. Methods Biomech. Biomed. Engin.
18, 1–14.
- 29. Buchanan, T.S., Lloyd, D.G., Manal, K.,
Besier, T.F., 2004. Neuromusculoskeletal
Modeling: Estimation of Muscle Forces and
Joint Moments and Movements from
Measurements of Neural Command. J. Appl.
Biomech. 20, 367–95.
- 30. Song, Z., Yi, J., Zhao, D., Li, X., 2005. A
Computed Torque Controller for Uncertain
Robotic Manipulator Systems: Fuzzy
Approach. Fuzzy Sets Syst. 154, 208–226.
- 31. Han, S., Wang, H., Tian, Y., 2017. Integral
Backstepping Based Computed Torque Control
for a 6 DOF Arm Robot. Proc. 29th Chinese
Control Decis. Conf. CCDC 2017 4055–4060.
doi:10.1109/CCDC.2017.7979210
- 32. Craig J.J., 2005. Introduction to Robotics.
Pearson Education Inc., doi:10.1111/j.1464-
410X.2011.10513.x
- 33. Ogata K., 2009. Modern Control Engineering.
Pearson Prentice Hall.
- 34. Chandrapal, M., Chen, X., 2009. Intelligent
Active Assistive and Resistive Orthotic Device
for Knee Rehabilitation. 2009 IEEE Int. Conf.
Control Autom. ICCA 2009 1880–1885
doi:10.1109/ICCA.2009.5410528
- 35. Cavallaro, E., Rosen, J., Perry, J.C., Burns, S.,
Hannaford, B., 2005. Hill-based Model as a
Myoprocessor for a Neural Controlled Powered
Exoskeleton Arm-Parameters Optimization.
Proc. - IEEE Int. Conf. Robot. Autom. 2005,
4514–4519.
Çok-Düzenli Robotik Kol Ortezinin Kas-iskelet Modeli Kullanılarak Tasarımı ve Performans Değerlendirmesi
Year 2018,
Volume: 33 Issue: 3, 133 - 144, 30.09.2018
Erkan Ödemiş
Cabbar Veysel Baysal
Abstract
Robotik kol ortezleri, motor-kas becerilerini kaybetmiş hastaların tedavisinde kullanılan ve başarıları sayısız klinik çalışmayla kanıtlanmış cihazlardır. Bu alandaki araştırmaların çoğu temel terapi düzeni operasyonlarını sağlayan ortotik cihazlara odaklanmıştır. Bununla birlikte terapi düzenlerini ve günlük aktiviteler için desteği uyumla gerçekleştirebilecek yeni ortotik cihaz tasarımlarına hala ihtiyaç vardır. Bu çalışmada çok düzenli, iki serbestlik derecesine sahip bir ortez tasarımı yapılmıştır. Tasarlanan ortez uyumlu çalışma becerisi açısından bir kas-iskelet modeli üzerinde benzetim ortamında denenmiştir. Ortez, model tabanlı hesaplamalı tork kontrolcü kullanmaktadır ve çok düzenli çalışma için test edilmiştir. Ortezin performansı “Yardımcı” ve “Dirençli” rehabilitasyon düzenlerinin uyumlu çalışması açısından değerlendirilmiştir. Performans testleri ilerde yapılacak geliştirmeler için cesaret verici sonuçlar vermektedir.
References
- 1. Volpe, B.T., Krebs, H.I., Hogan, N., 2001. Is
Robot-aided Sensorimotor Training in Stroke
Rehabilitation a Realistic Option? Curr. Opin.
Neurol. 14, 745–752.
- 2. Lum, P.S., Burgar, C.G., Shor, P.C.,
Majmundar, M., Van der Loos, M., 2002.
Robot-assisted Movement Training Compared
with Conventional Therapy Techniques for the
Rehabilitation of Upper-limb Motor Function
After Stroke. Arch. Phys. Med. Rehabil. 83,
952–959.
- 3. Gopura, R.A.R.C., Kiguchi, K., 2009.
Mechanical Designs of Active Upper-limb
Exoskeleton Robots State-of-the-art and
Design Difficulties. 2009 IEEE Int. Conf.
Rehabil. Robot. ICORR 2009 178–187
doi:10.1109/ICORR.2009.5209630.
- 4. Gopura, R.A.R.C., Bandara, D.S.V., Kiguchi,
K., Mann, G.K.I. 2016. Developments in
Hardware Systems of Active Upper-limb
Exoskeleton Robots: A review. Rob. Auton.
Syst. 75, 203–220
- 5. Marchal-Crespo, L., Reinkensmeyer, D.J.,
2009. Review of Control Strategies for Robotic
Movement Training After Neurologic Injury. J.
Neuroeng. Rehabil. 6, 20.
- 6. Wolbrecht, E.T., Chan, V., Reinkensmeyer, D.
J., Bobrow, J.E. 2008. Optimizing Compliant,
Model-based Robotic Assistance to Promote
Neurorehabilitation. IEEE Trans. Neural Syst.
Rehabil. Eng. 16, 286–297.
- 7. Lo, H.S., Xie, S.Q., 2012. Exoskeleton Robots
for Upper-limb Rehabilitation: State of the Art
and Future Prospects. Med. Eng. Phys. 34,
261–268.
- 8. Anam, K., Al-Jumaily, A.A., 2012. Active
Exoskeleton Control Systems: State of the Art.
Procedia Eng. 41, 988–994.
- 9. Lee, H.D., Lee, B.K., Kim, W.S., Han, J.S.,
Shin, K.S., Han, C.S., 2014. Human-robot
Cooperation Control Based on a Dynamic
Model of an Upper Limb Exoskeleton for
Human Power Amplification. Mechatronics 24,
168–176.
- 10. Rosen, J., Brand, M., Fuchs, M.B., Arcan, M.,
2001. A Myosignal-based Powered
Exoskeleton System. IEEE Trans. Syst. Man,
Cybern. Part ASystems Humans. 31, 210–222.
- 11. Sugar, T.G., He, J., Koeneman, E.J.,
Koeneman, J.B., Herman, R., Huang, H.,
Schultz, R.S., Herring, D.E., Wanberg, J.,
Balasubramanian, S., Swenson, P., Ward, J.A.,
2007. Design and Control of RUPERT: A
Device for Robotic Upper Extremity Repetitive
Therapy. IEEE Trans. Neural Syst. Rehabil.
Eng. 15, 336–346.
- 12. Gopura, R.A.R.C., Kiguchi, K., Yi, Y., 2009.
SUEFUL-7: A 7DOF Upper-limb Exoskeleton
Robot with Muscle-model-oriented EMGBased
Control. 2009 IEEE/RSJ Int. Conf.
Intell. Robot. Syst. IROS 2009 1126–1131,
doi:10.1109/IROS.2009.5353935
- 13. Crema, A., Mancuso, M., Frisoli, A., Selsedo
F., Raschella, F., Micea, S., 2015. A Hybrid
NMES-exoskeleton for Real Objects
Interaction. Int. IEEE/EMBS Conf. Neural
Eng. NER 2015–July, 663–666.
- 14. Carignan, C., Tang, J., Roderick, S., 2009.
Development of an Exoskeleton Haptic
Interface for Virtual Task Training. 2009
IEEE/RSJ Int. Conf. Intell. Robot. Syst. IROS
2009 3697–3702, doi:10.1109/IROS.2009.
5354834
- 15. Davoodi, R., Loeb, G. E., 2011. MSMS
Software for VR Simulations of Neural
Prostheses and Patient Training and
Rehabilitation. Stud. Health Technol. Inform.
163, 156–162.
- 16. Moromizato, K., Kimura, R., Fukase, H.,
Yamaguchi, K., Ishida, H., 2016. Whole-body
Patterns of the Range of Joint Motion in Young Adults: Masculine Type and Feminine Type. J.
Physiol. Anthropol. 35, 23.
- 17. Lewis F.L., Munro N., 2004. Robot
Manipulator Control Theory and Practice.
Marcel Dekker, Inc.
- 18. Delp, S. L., Loan, J. P., Hoy, M. G., Zajac, F.
E., Topp, E.L., Rosen, J.M., 1990. An
Interactive Graphics-based Model of the Lower
Extremity to Study Orthopedic Surgical
Procedures. IEEE Trans. Biomed. Eng. 37,
757–767.
- 19. Delp, S.L., Anderson, F.C., Arnold, A.S.,
Loan, P., Habib, A., John, C.T., Guendelman,
E., Thelen, D.G., 2007. OpenSim: Open Source
to Create and Analyze Dynamic Simulations of
Movement. IEEE Trans. Biomed. Eng. 54,
1940–1950.
- 20. Damsgaard, M., Rasmussen, J., Christensen,
S.T., Surma, E., de Zee, M., 2006. Analysis of
Musculoskeletal Systems in the AnyBody
Modeling System. Simul. Model. Pract. Theory
14, 1100–1111.
- 21. Cheng, E.J., Brown, I.E., Loeb, G.E., 2001.
Virtual Muscle: A Computational Approach to
Understanding the Effects of Muscle Properties
on Motor Control. J. Neurosci. Methods 106,
111–112.
- 22. Rosen, J., Fuchs, M. B., Arcan, M., 1999.
Performances of Hill-Type and Neural
Network Muscle Models-Toward a Myosignal-
Based Exoskeleton. Comput. Biomed. Res. 32,
415–439.
- 23. Zajac, F.E., 1989. Muscle and Tendon:
Properties, Models, Scaling, and Application to
Biomechanics and Motor Control. Crit. Rev.
Biomed. Eng. 17, 359–411.
- 24. Winters, J. M., 1995. An Improved Muscle-
Reflex Actuator for use in Large-scale
Neuromusculoskeletal Models. Ann. Biomed.
Eng. 23, 359–374.
- 25. Garner, B.A., Pandy, M.G., 2001.
Musculoskeletal Model of the Upper Limb
Based on the Visible Human Male Dataset.
Comput. Methods Biomech. Biomed. Engin. 4,
93–126.
- 26. Langenderfer, J., Jerabek, S.A., Thangamani,
V.B., Kuhn, J.E., Hughes, R.E., 2004.
Musculoskeletal Parameters of Muscles
Crossing the Shoulder and Elbow and the
Effect of Sarcomere Length Sample Size on
Estimation of Optimal Muscle Length. Clin.
Biomech. 19, 664–670.
- 27. Holzbaur, K.R.S., Murray, W.M., Delp, S.L.,
2005. A Model of the Upper Extremity for
Simulating Musculoskeletal Surgery and
Analyzing Neuromuscular Control. Ann.
Biomed. Eng. 33, 829–840.
- 28. Saul, K.R., Hu X., Goehler C.M., Vidt M.E.,
Daly M., Velisar A., Murray W.M., 2014.
Benchmarking of Dynamic Simulation
Predictions in Two Software Platforms using
an Upper Limb Musculoskeletal Model.
Comput. Methods Biomech. Biomed. Engin.
18, 1–14.
- 29. Buchanan, T.S., Lloyd, D.G., Manal, K.,
Besier, T.F., 2004. Neuromusculoskeletal
Modeling: Estimation of Muscle Forces and
Joint Moments and Movements from
Measurements of Neural Command. J. Appl.
Biomech. 20, 367–95.
- 30. Song, Z., Yi, J., Zhao, D., Li, X., 2005. A
Computed Torque Controller for Uncertain
Robotic Manipulator Systems: Fuzzy
Approach. Fuzzy Sets Syst. 154, 208–226.
- 31. Han, S., Wang, H., Tian, Y., 2017. Integral
Backstepping Based Computed Torque Control
for a 6 DOF Arm Robot. Proc. 29th Chinese
Control Decis. Conf. CCDC 2017 4055–4060.
doi:10.1109/CCDC.2017.7979210
- 32. Craig J.J., 2005. Introduction to Robotics.
Pearson Education Inc., doi:10.1111/j.1464-
410X.2011.10513.x
- 33. Ogata K., 2009. Modern Control Engineering.
Pearson Prentice Hall.
- 34. Chandrapal, M., Chen, X., 2009. Intelligent
Active Assistive and Resistive Orthotic Device
for Knee Rehabilitation. 2009 IEEE Int. Conf.
Control Autom. ICCA 2009 1880–1885
doi:10.1109/ICCA.2009.5410528
- 35. Cavallaro, E., Rosen, J., Perry, J.C., Burns, S.,
Hannaford, B., 2005. Hill-based Model as a
Myoprocessor for a Neural Controlled Powered
Exoskeleton Arm-Parameters Optimization.
Proc. - IEEE Int. Conf. Robot. Autom. 2005,
4514–4519.