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A Control Framework of Lower Extremity Rehabilitation Exoskeleton Based on Neuro-Muscular-Skeletal Model


Affiliations
1 Graduate School of Engineering, Nagasaki Institute of Applied Science, Aba-machi, Nagasaki, Japan
2 Department of Advanced Technology and Science for Sustainable Development, Nagasaki University, Bunnkyomachi, Japan
     

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A control system framework of lower extremity rehabilitation exoskeleton robot is presented. It is based on the Neuro-Musculo-Skeletal biological model. Its core composition moudle, the motion intent parser part, mainly comprises of three distinct parts.The first part is signal acquisition of surface electromyography (sEMG) that is the summation of motor unit action potential (MUAP) starting from central nervous system (CNS). sEMG can be used to decode action intent of operator to make the patient actively participate in specific training. As another composition part, a muscle dynamics model that is comprised of activation and contraction dynamic model is developed. It is mainly used to calculate muscle force. The last part is the skeletal dynamic model that is simplified as a linked segment mechanics. Combined with muscle dynamic model, the joint torque exerted by internal muscles can be exported, which can be ued to do a exoskeleton controller design. The developed control framework can make exoskeleton offer assistance to operators during rehabilitation by guiding motions on correct training rehabilitation trajectories, or give force support to be able to perform certain motions. Though the presentation is orientated towards the lower extremity exoskeleton, itis generic and can be applied to almost any part of the human body.

Keywords

Rehabilitation Exoskeleton, Surface Electromyography (sEMG), Neuro-Musculo-Skeletal Model, Muscle Dynamics Model, Skeletal Model.
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  • Banala, S. K., Kim, S. H., Agrawal, S. K., & Scholz, J. P. (2009). Robot assisted gait training with active leg exoskeleton (ALEX). IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17(1), 2-8.

  • Cavanagh, P., & Komi, P. (1979). Electromechanical delay in human skeletal muscle under concentric and eccentric contractions. European Journal of Applied Physiology and Occupational Physiology, 42 (3), 159-163.

  • De Luca, C., LeFever, R., McCue, M., & Xenakis, A.(1982). Control scheme governing concurrently active human motor units during voluntary contractions.The Journal of Physiology, 329 (1), 129-142.

  • Farris, R. J., Quintero, H. A., & Goldfarb, M. (2011).Preliminary evaluation of a powered lower limb orthosis to aid walking in paraplegic individuals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19 (6), 652-659.

  • Ferris, D. P., & Lewis, C. L. (2009). Robotic lower limb exoskeletons using proportional myoelectric control.Conference Proceedings on IEEE Engineering in Medicine and Biology Society, 2119-2124.

  • Fleischer, C. (2007). Controlling exoskeletons with EMG signals and a biomechanical body model. Scuola Superiore Sant’Anna.

  • Greenwood, D. T. (1997). Classical dynamics. Courier Corporation.

  • Hill, A. (1938). The heat of shortening and the dynamic constants of muscle. Proceedings of the Royal Society of London B: Biological Sciences, 126 (843), 136-195.

  • Husemann, B., Müller, F., Krewer, C., Heller, S., & Koenig, E. (2007). Effects of locomotion training with assistance of a robot-driven gait orthosis in hemiparetic patients after stroke a randomized controlled pilot study. Stroke, 38 (2), 349-354.

  • Jezernik, S., Colombo, G., Keller, T., Frueh, H., & Morari, M. (2003). Robotic orthosis lokomat: A rehabilitation and research tool. Neuromodulation: Technology at the Neural Interface, 6 (2), 108-115.

  • Kaelin-Lang, A., Sawaki, L., & Cohen, L. G. (2004). Role of voluntary drive in encoding an elementary motor memory. Journal of Neurophysiology, February, 93 (2), 1099-1103.

  • Knudson, D. (2007). Fundamentals of biomechanics (2nded. ). Springer Science & Business Media.

  • Kong, K., & Jeon, D. (2006). Design and control of an exoskeleton for the elderly and patients. IEEE/ASME Transactions on Mechatronics, 11 (4), 428-432.

  • Lee, J. W., & Lee, G. K. (2005). Gait angle prediction for lower limb orthotics and prostheses using an EMG signal and neural networks. International Journal of Control, Automation, and Systems, 3 (2), 152-158.

  • Kun, L. (2010). Research on Wearable Sensor system for 3D lower limb kinematics analysis. (doctor [D]), Kochi University of Technology, Kochi, Japan.

  • Lotze, M., Braun, C., Birbaumer, N., Anders, S., & Cohen, L. G. (2003). Motor learning elicited by voluntary drive. Brain, 126 (4), 866-872.

  • Merletti, R., & Parker, P. A. (2004). Electromyography: Physiology, engineering, and non-invasive applications. Vol. 11, John Wiley & Sons.

  • Park, J., & Khatib, O. (2006). A haptic teleoperation approach based on contact force control. The International Journal of Robotics Research, 25 (56), 575-591.

  • Petersen, K., Hansen, C. B., Aagaard, P., & Madsen, K. (2007). Muscle mechanical characteristics in fatigue and recovery from a marathon race in highly trained runners. European Journal of Applied Physiology, 101 (3), 385-396.

  • Pons, J. L. (2008). Wearable robots: Biomechatronic exoskeletons.Vol. 70, Wiley Online Library.

  • Riener, R. (2012). Technology of the Robotic Gait Orthosis Lokomat Neurorehabilitation Technology, (pp. 221-232), Springer.

  • Rosen, J., Fuchs, M. B., & Arcan, M. (1999). Performances of Hill-type and neural network muscle modelstoward a myosignal-based exoskeleton. Computers and Biomedical Research, 32 (5), 415-439.

  • Sankai, Y. (2011). HAL: Hybrid assistive limb based on cybernics Robotics Research (pp. 25-34), Springer.

  • Schouenborg, J. (2004). Learning in sensorimotor circuits. Current Opinion in Neurobiology, 14 (6), 693-697.

  • Schultz, A. E., & Kuiken, T. A. (2011). Neural interfaces for control of upper limb prostheses: The state of the art and future possibilities. PMR, 3(1), 55-67.

  • Shi, L., & Liu, Z. (2014). Design of Soft Human-Robot Interface Based on Neuro-Muscular-Skeletal Model. IEEE 12th International Conference on paper presented at the Dependable, Autonomic and Secure Computing (DASC).

  • Shi, L., Liu, Z., & Wang, Q. (2013). A Novel Method of sEMG Signal Segmentation. IEEE Ninth International Conference on Mobile Ad-hoc and Sensor Networks (MSN).

  • Thelen, D. G. (2003). Adjustment of muscle mechanics model parameters to simulate dynamic contractions in older adults. Journal of Biomechanical Engineering, 125 (1), 70.

  • Unluhisarcikli, O., Pietrusinski, M., Weinberg, B., Bonato, P., & Mavroidis, C. (2011). Design and Control of a Robotic Lower Extremity Exoskeleton for Gait Rehabilitation. Paper presented at the Intelligent Robots and Systems (IROS), 2011 IEEE/ RSJ International Conference on.

  • van Asseldonk, E. H., & van der Kooij, H. (2012). Robot-Aided Gait Training with LOPES.Neurorehabilitation Technology (pp. 379-396), Springer.

  • Veneman, J. F., Kruidhof, R., Hekman, E. E., Ekkelenkamp, R., Van Asseldonk, E. H., & Van Der Kooij, H. (2007). Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation.IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15(3), 379-386.

  • Westlake, K. P., & Patten, C. (2009). Pilot study of Lokomat versus manual-assisted treadmill training for locomotor recovery post-stroke. Journal of Neuro-Engineering and Rehabilitation, 6 (18).

  • Winter, D. A. (1980). Overall principle of lower limb support during stance phase of gait. Journal of Biomechanics, 13 (11), 923-927.

  • Winter, D. A. (1984). Kinematic and kinetic patterns in human gait: Variability and compensating effects

  • Human Movement Science, 3 (1), 51-76.

  • Winters, J. M. (1995). An improved muscle-reflex actuator for use in large-scale neuromusculo skeletal models. Annals of Biomedical Engineering, 23 (4), 359-374.

  • Zhou, S., Lawson, D. L., Morrison, W. E., & Fairweather, I. (1995). Electromechanical delay in isometric muscle contractions evoked by voluntary, reflex and electrical stimulation. European Journal of Applied Physiology and Occupational Physiology, 70 (2), 138-145.


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  • A Control Framework of Lower Extremity Rehabilitation Exoskeleton Based on Neuro-Muscular-Skeletal Model

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Authors

Lei Shi
Graduate School of Engineering, Nagasaki Institute of Applied Science, Aba-machi, Nagasaki, Japan
Zhen Liua
Graduate School of Engineering, Nagasaki Institute of Applied Science, Aba-machi, Nagasaki, Japan
Chao Zhang
Department of Advanced Technology and Science for Sustainable Development, Nagasaki University, Bunnkyomachi, Japan

Abstract


A control system framework of lower extremity rehabilitation exoskeleton robot is presented. It is based on the Neuro-Musculo-Skeletal biological model. Its core composition moudle, the motion intent parser part, mainly comprises of three distinct parts.The first part is signal acquisition of surface electromyography (sEMG) that is the summation of motor unit action potential (MUAP) starting from central nervous system (CNS). sEMG can be used to decode action intent of operator to make the patient actively participate in specific training. As another composition part, a muscle dynamics model that is comprised of activation and contraction dynamic model is developed. It is mainly used to calculate muscle force. The last part is the skeletal dynamic model that is simplified as a linked segment mechanics. Combined with muscle dynamic model, the joint torque exerted by internal muscles can be exported, which can be ued to do a exoskeleton controller design. The developed control framework can make exoskeleton offer assistance to operators during rehabilitation by guiding motions on correct training rehabilitation trajectories, or give force support to be able to perform certain motions. Though the presentation is orientated towards the lower extremity exoskeleton, itis generic and can be applied to almost any part of the human body.

Keywords


Rehabilitation Exoskeleton, Surface Electromyography (sEMG), Neuro-Musculo-Skeletal Model, Muscle Dynamics Model, Skeletal Model.

References