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Design and Evaluation of a Multi-Mode Robotic Arm Orthosis using Musculoskeletal Simulation

Yıl 2018, Cilt: 33 Sayı: 3, 133 - 144, 30.09.2018
https://doi.org/10.21605/cukurovaummfd.504557

Öz

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. 

Kaynakça

  • 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.

Çok-Düzenli Robotik Kol Ortezinin Kas-iskelet Modeli Kullanılarak Tasarımı ve Performans Değerlendirmesi

Yıl 2018, Cilt: 33 Sayı: 3, 133 - 144, 30.09.2018
https://doi.org/10.21605/cukurovaummfd.504557

Öz

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. 

Kaynakça

  • 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.
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mimarlık, Mühendislik
Bölüm Makaleler
Yazarlar

Erkan Ödemiş Bu kişi benim

Cabbar Veysel Baysal

Yayımlanma Tarihi 30 Eylül 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 33 Sayı: 3

Kaynak Göster

APA Ödemiş, E., & Baysal, C. V. (2018). Design and Evaluation of a Multi-Mode Robotic Arm Orthosis using Musculoskeletal Simulation. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 33(3), 133-144. https://doi.org/10.21605/cukurovaummfd.504557
AMA Ödemiş E, Baysal CV. Design and Evaluation of a Multi-Mode Robotic Arm Orthosis using Musculoskeletal Simulation. cukurovaummfd. Eylül 2018;33(3):133-144. doi:10.21605/cukurovaummfd.504557
Chicago Ödemiş, Erkan, ve Cabbar Veysel Baysal. “Design and Evaluation of a Multi-Mode Robotic Arm Orthosis Using Musculoskeletal Simulation”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 33, sy. 3 (Eylül 2018): 133-44. https://doi.org/10.21605/cukurovaummfd.504557.
EndNote Ödemiş E, Baysal CV (01 Eylül 2018) Design and Evaluation of a Multi-Mode Robotic Arm Orthosis using Musculoskeletal Simulation. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 33 3 133–144.
IEEE E. Ödemiş ve C. V. Baysal, “Design and Evaluation of a Multi-Mode Robotic Arm Orthosis using Musculoskeletal Simulation”, cukurovaummfd, c. 33, sy. 3, ss. 133–144, 2018, doi: 10.21605/cukurovaummfd.504557.
ISNAD Ödemiş, Erkan - Baysal, Cabbar Veysel. “Design and Evaluation of a Multi-Mode Robotic Arm Orthosis Using Musculoskeletal Simulation”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 33/3 (Eylül 2018), 133-144. https://doi.org/10.21605/cukurovaummfd.504557.
JAMA Ödemiş E, Baysal CV. Design and Evaluation of a Multi-Mode Robotic Arm Orthosis using Musculoskeletal Simulation. cukurovaummfd. 2018;33:133–144.
MLA Ödemiş, Erkan ve Cabbar Veysel Baysal. “Design and Evaluation of a Multi-Mode Robotic Arm Orthosis Using Musculoskeletal Simulation”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, c. 33, sy. 3, 2018, ss. 133-44, doi:10.21605/cukurovaummfd.504557.
Vancouver Ödemiş E, Baysal CV. Design and Evaluation of a Multi-Mode Robotic Arm Orthosis using Musculoskeletal Simulation. cukurovaummfd. 2018;33(3):133-44.