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Development of the Neuromechanics Evaluation Device (NED) for Subject-Specifc Lower Limb Modelling of Spinal Cord Injury

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Development of the Neuromechanics Evaluation Device (NED) for Subject-Specifc Lower Limb Modelling of Spinal Cord Injury Imperial College of Science, Technology and Medicine Department of Bioengineering Development of the neuromechanics evaluation device (NED) for subject-specifc lower limb modelling of spinal cord injury Hsien-Yung, Huang Submitted in part fulflment of the requirements for the degree of Doctor of Philosophy in Bioengineering of Imperial College London and the Diploma of Imperial College London, October 2018 October, 2018 This is to certify that the work in this thesis has been carried out at the Department of Bio- engineering, Imperial College of Science, Technology and Medicine and has not been previously submitted to any other university or technical institution for a degree or award. The thesis comprises only my original work, except where due acknowledgement is made in the text. The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work. 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be ob- tained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 3 Abstract Investigating human neuromechanics can be used to characterise motor impairments in neuro- logically afected individuals, and can give insight into how the brain controls movement. In particular, the lower limbs' neuromechanics is critical to assess the balance control and mobility. However, there are still few experimental results on the neuromechanics of the lower limb. This is especially true of the hip, in part due to the difculty to quickly accelerate the heavy leg in a controlled manner. Furthermore, existing robotic interfaces for the lower limb typically con- strain the joints motion and cannot provide the quick and smooth perturbations necessary to identify subject's biomechanics. In this context, this thesis presents: i) a new robotic interface that have developed to measure lower-limb neuromechanics, ii) a systematic investigation of the hip viscoelasticity carried out with this interface, and iii) a biomechanical model that provides subject-specifc dynamic behaviour based on parameters from neuromechanics experiments. The Neuromechanics Evaluation Device (NED) is an endpoint-based cable-driven robotic in- terface that can be used to measure the hip, knee and ankle joint neuromechanics. Subjects can take an upright posture, which is important for subjects with weak motor condition, while the interface moves the limb's extremity over a large workspace without constraining the joint orientation. Rapid position displacements can be applied to the studied limb by the powerful actuator fxed to the ground. Mechanical evaluations showed that NED has a rigidity above 500N/m and viscosity below 50Ns/m. It is also able to produce fast perturbations (e.g. a displacement of 2cm within 230ms) without vibration, which can be used to identify lower-limb neuromechanics. The ability of NED to carry out various neuromechanics measurements is illustrated in two experiments carried out with healthy subjects. First, a measurement of the maximum voluntary torque at the hip joint yielded values in line with reported estimates in the literature. Second, a systematic investigation of the hip joint viscoelasticity was carried out with 10 subjects. In line with previous fndings on the upper and lower limbs, hip stifness was found to monotonically increase with the applied force, with a slight dependence on the hip angle. These experiments exemplify how NED can be efciently used to characterise the lower-limb joint neuromechanics. The thesis further presents a model of lower limb neuromechanics integrating subject-specifc parameters that can be identifed through experiments with a robotic interface. The proposed 4 model incorporates physiological parameters such as the torque-angle and the torque-angular velocity dependencies, as well as the joint viscoelasticity. The model was used to evaluate typical neuromechanics alterations caused by a spinal cord injury. In addition, the infuence of various neuromechanical joint parameters to postural control was evaluated in simulations. 5 6 Acknowledgements I would like to thank my supervisor Professor Etienne Burdet who provided me with the chance to join the Symbitron project and develop the big robot NED. I would also like to thank my second supervisors Ildar, Arash and Andrew, who have always been supportive during the project and provides essential suggestions. The discussions and brainstormings we had made great changes to my future and I really cherish this experience. Human Robotics Group has always been a big family to me. For all those who have been in this family: Alessandro, Alfredo, Atsushi, Audrey, Carlo, Consuelo, Elif, Elisabeth, Eugenie, Francesca, Franck, Gero, Ildar, Jin, Jonathan, Martina, Matjaz, Mike, Milena, Moritz, Nuria, Paul, Paulo, Pierre-Jean, Sarah, Sean, Sharah, Shou-han, Sofa, Xiaodong, Yanan. Thank you for the time we had together. Additionally, I would like to thank Professor Dario Farina, to share the space with me to develop NED. Also, the Neuromechanics and Rehabilitation Technology Group had shared me light while we are all in a lab located down in the basement, without a window. For all those who have been in this lab: Alessandro, Andrea, Carina, Christos, Corrado, Deren, Emanuele, Emiliano, Emma, Federico, Giulia, Giuseppe, Gonthicha, Guglielmo, Irene, Ivan, Margherita, Mario, Markus, Markus, Martyna, Matteo, Sigrid, Silvia, Simone. The time we had together is memorable. Many thanks to all my friends. Your passions and energy made me felt warm and welcomed in a foreign land called London. It is all of you who reminds me who I am and what should I become. Most important of all, I would like to thank my family and my beloved Minmin for all the supports during my quest towards my PhD. Nothing could have happened without the support and guidance you gave me. I cannot accomplish until this stage without you all. This thesis is dedicated to you. 7 Acronym BF Bicep Femoris CAD Computer aided design CNS Central nervous system CPG Central pattern generators DOF Degree of freedom EMG Electromyography HW model Hammerstein-Wiener model MVC Maximum voluntary contraction MVJT Maximum voluntary joint torque MVIC maximal voluntary isometric contractions NED Neuromechanics Evaluation Device NRMSE Normalized root mean square error PC Personal computer PIC controller Proportionalintegralderivative controller RF Rectus Femoris ROM Range of motion SCI Spinal cord injury TA Tibialis Anterior 8 Contents Abstract 4 Acknowledgements 7 Acronyms 7 1 Introduction 19 2 A lower limb Neuromechanics Evaluation Device (NED) 23 2.1 Overview . 23 2.2 Introduction . 24 2.2.1 Existing neuromechanics estimation devices . 24 2.2.2 Functional requirements . 26 2.3 Device design . 27 2.3.1 General description . 27 2.3.2 Cable transmission . 29 2.3.3 Control system . 31 2.3.4 Safety measure and ergonomics . 32 9 10 CONTENTS 2.4 System characterisation . 33 2.4.1 Kinematics and sensitivity analysis . 33 2.4.2 Spatial and temporal dependency of cable tension . 35 2.4.3 Cable temporal dependency . 37 2.4.4 Cable system modelling . 38 2.4.5 Cable Nonlinearities . 40 2.5 Validation . 41 2.5.1 Dummy leg mechanics . 41 2.5.2 Stifness estimation . 43 2.5.3 Optimal position perturbation to identify stifness . 44 2.6 Discussion . 47 3 Hip joint neuromechanics evaluation with cable driven robot NED 49 3.1 Maximal voluntary isometric contractions (MVIC) . 49 3.1.1 Experiment protocol . 50 3.1.2 Results . 52 3.1.3 Discussion . 54 3.2 The infuence of posture, applied force and perturbation direction on hip joint viscoelasticity . 55 3.2.1 Literature . 55 3.2.2 Methods . 57 3.2.3 Results . 62 CONTENTS 11 3.2.4 Discussion . 68 4 Subject-specifc modelling and evaluation 71 4.1 Literature review . 72 4.1.1 Physiological alterations following spinal cord injury . 72 4.1.2 Hill's type muscle model . 76 4.1.3 Inverted pendulum model . 78 4.2 Subject-specifc modelling . 80 4.2.1 Model description . 80 4.2.2 Method to identify the model's parameters . 82 4.3 Model evaluation using a single-joint inverted pendulum simulation . 84 4.3.1 Model parameter . 84 4.3.2 Scenario description and evaluation methods . 86 4.3.3 Human balance simulation with subject-specifc modelling . 90 4.3.4 Parameter sensitivity analysis and robustness to muscle noise . 93 4.3.5 Balance experiment with LOPES . 100 4.3.6 Discussion . 100 5 Conclusion 102 Bibliography 104 A Motor selection 115 B Ethical approval 117 12 List of Tables 2.1 Characteristics of existing lower limb neuromechanics evaluation devices . 25 2.2 Past list . 30 3.1 Biographical information of the subject in MVIC experiment . 51 3.2 Biographical information of the subjects in hip joint impedance measurements . 59 3.3 Statistics of linear regression and mixed efect models . 69 4.1 Values of the model's parameters and relations . 86 13 14 List of Figures 2.1 Description of the Neuromechanics Evaluation Device (NED) . 28 2.2 The control system of NED . 32 2.3 Laser safety system . 33 2.4 Of-plane motion induced error . 34 2.5 Cable sagging . 36 2.6 Temporal characteristics of NED . 38 2.7 Identifcation of NED as a linear second order system . 39 2.8 Identifcation of NED as a linear second order system . 40 2.9 Identifcation of the mechanics of a 18kg dummy leg .
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