In people with neuromotor deficits of trunk and lower extremities, maintaining and regaining balance is a difficult task. Many undergo rehabilitation to improve their movement capabilities, health, and overall interactions with their environment. Rehabilitation consists of a set of interventions designed to improve the individual’s mobility and independence. These strategies can be passive, active or task-specific and are dependent on the type of injury, how the individual progresses, and the intensity of the activity. Some of the common rehabilitation interventions to strengthen muscles and improve coordination are accomplished either by the manual assistance of a physical therapist, bodyweight suspension systems or through robotic-assisted training. There are several types of rehabilitation robotic systems and robotic control strategies.However, there are few robotic studies that compare their robotic device’s control strategy to common rehabilitation interventions. This dissertation introduces robotic strategies centered around rehabilitation ones and characterizes human motion in response to the robotic forces. Two cable-driven robotic systems are utilized to implement the robotic controllers for different tasks. Further details of the two cable-driven systems are discussed in Chapter 1. The validation and evaluation of these robotic strategies for standing rehabilitation is discussed in Chapter 2. A case study of a robotic training paradigm for individuals with spinal cord injury is presented in Chapter 3. Chapter 4 introduces a method to redistribute individuals’ weight using pelvic lateral forces. Chapter 5 and 6 characterizes how young and older groups respond to external perturbations during their sit-to-stand motion. This dissertation presents robotic strategies that can be implemented as rehabilitation interventions. It also presents how individuals’ biomechanics and muscle responses may change depending on the force control paradigm.These robotic strategies can be utilized by training individuals to improve their reactive and active balance control and thus reduce their risk of falling.

重症肌無力是由自身抗體在神經肌肉接頭特異性的結合乙酰膽鹼受體和肌肉特異性激酶引起的一種獲得性免疫性疾病。疾病的主要症狀是骨骼肌的軟弱無力和易疲勞性。這一症狀在運動後尤為顯著，休息後會有所緩解。重症肌無力在世界範圍的發病率是百萬分之三到三十。由於近年來患者的數量在不斷增加，重症肌無力引起了醫學界的廣泛關注。但是，目前的診斷和治療措施還不能完全滿足臨床病人的需要。在本課題研究中，我們希望運用代謝組學的手段建立一種新的更加有效可靠的方法用於重症肌無力的診斷。同時，我們希望在代謝物的水平上來闡釋強肌健力方（一種中藥復方）對重症肌無力的治療作用。 / 本研究所用樣本來自42個重症肌無力病人和16個健康志願者。樣本由廣州中醫藥大學第一附屬醫院於二零零七年到二零零八年收集所得。診斷後，病人每日口服一定劑量的強肌健力方接受治療，連續服藥兩個月。分別在服藥前和治療後對病人抽血採樣。進一步分離血清後，樣品進行質譜分析。多元統計學方法如主成分分析，正交偏最小二乘和正交偏最小二乘判別分析等用於質譜數據的分析。 / 通過和健康者比較分析，我們在重症肌無力病人的血液中找到142個顯著改變的離子。其中，14個離子得到鑒定，包括：γ-氨基丁酸，2-哌啶酸，鳥氨酸，5,8-十四碳二羧酸，精胺，己酰肉毒鹼，N-油酰基甘氨酸，鞘氨醇-1-磷酸，聯原膽酸，糞甾烷酸，植物鞘氨醇-1-磷酸，鵝去氧膽酸甘氨酸結合物，輔酶Q4和甘氨酸膽。基於以上142個離子建立的數學診斷模型在診斷重症肌無力時表現出很高的靈敏度和特異性，分別高達92.8%和83.3%。強肌健力方能夠逆轉由重症肌無力引起的特異性代謝變化，將病人體內被改變的代謝網絡恢復正常，特別是大部分的代謝標誌物在治療後都恢復到了相對正常水平，包括：γ-氨基丁酸，哌啶酸，鳥氨酸，5,8-十四碳二羧酸，精胺，己酰肉毒鹼，N-油酰甘氨酸，鞘氨醇-1-磷酸，聯原膽酸，輔酶Q4和甘氨酸膽。 / 本研究揭示了基於液質聯用的代謝組學方法適用於探索重症肌無力的代謝標誌物，並提供了一種可用於診斷重症肌無力的新方法。同時，本研究證實強肌健力方適用於重症肌無力的治療，且無明顯副作用。 / Myasthenia gravis (MG) is an acquired autoimmune disease caused by specific autoantibodies against acetylcholine receptors (AChRs) and muscle-specific kinase (MuSK) proteins at the neuromuscular junctions. The disease is characterized by weakness and fatigability of the voluntary muscles that gets worse with exertion and improves with rest. The global incidence rate of MG is about 3-30 cases per million per year. In recent years, the worldwide prevalence rate of MG is increasing as a result of increased awareness. However, current diagnostic measures and treatments are not conclusive and satisfactory for MG. In this study, a mass spectrometry-based metabolomic strategy was applied to develop a novel and reliable diagnostic measure for MG on the basis of metabolic analysis, and to explore the therapeutic effect of Qiangji Jianli Fang (QJF, a newly developed Chinese medicine formula) on MG at the metabolite level. / Total 42 MG patients (13 males and 29 females) and 16 volunteers (5 males and 11 females) were recruited at the First Affiliated Hospital of Guangzhou University of Chinese Medicine between March 2007 and March 2008. The patients took QJF once per day for 2 months. Peripheral blood from patients was collected at diagnosis and after 2-month treatment, respectively. Sera prepared from the blood samples were monitored by the liquid chromatography Fourier transform mass spectrometry (LC-FTMS). Mass spectral data were analyzed by multivariate statistical analyses, including principal component analysis (PCA), orthogonal partial least squares (OPLS), and orthogonal partial least squares discriminant analysis (OPLS-DA). / By comparing analysis with the healthy volunteers, 142 significantly changed ions from serum metabolic profile of MG patients were picked out as the potential biomarkers of MG. Among of them, 14 ions were temporarily identified. They were gamma-aminobutyric acid (GABA), pipecolic acid, ornithine, 5,8-tetradecadienoic acid, spermine, hexanoylcarnitine, N-oleoyl glycine, sphingosine-1-phosphate (S1P), bisnorcholic acid, coprocholic acid, phytosphingosine-1-P, chenodeoxycholylglycine, coenzyme Q4, and cholylglycine. The developed OPLS-DA diagnostic model based on the 142 special ions showed a high sensitivity (92.8%) and specificity (83.3%) in detecting MG. QJF showed a powerful action on MG by recovering the holistic serum metabolic profile from the disease level to the normal level. Especially, the levels of GABA, pipecolic acid, ornithine, 5,8-tetradecadienoic acid, spermine, hexanoylcarnitine, N-oleoyl glycine, S1P, bisnorcholic acid, coenzyme Q4, and cholylglycine in MG patients were regulated to a relatively normal level after QJF treatment. / My results first indicated that the LC-FTMS-based metabolomics was a useful tool in biomarkers exploration of MG, and it was potentially applicable as a new diagnostic approach for MG. Also, my results demonstrated that QJF was a good optional choice for the treatment of MG, with no reported side effects. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Lu, Yonghai. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 113-129). / Abstract also in Chinese. / Thesis committee --- p.i / Declaration --- p.ii / Abstract (in English) --- p.iii / Abstract (in Chinese) --- p.vi / Acknowledgements --- p.viii / Table of contents --- p.ix / Abbreviations --- p.xiv / List of Tables --- p.xviii / List of Figures --- p.xix / Chapter 1: Introduction --- p.1 / Chapter 1.1 --- Myasthenia gravis --- p.1 / Chapter 1.1.1 --- History --- p.1 / Chapter 1.1.2 --- Epidemiology --- p.2 / Chapter 1.1.3 --- Clinical features --- p.2 / Chapter 1.1.4 --- Clinical classification --- p.4 / Chapter 1.1.5 --- Pathophysiology --- p.5 / Chapter 1.1.6 --- Diagnosis --- p.9 / Chapter 1.1.6.1 --- Physical examination --- p.9 / Chapter 1.1.6.2 --- Blood test --- p.10 / Chapter 1.1.6.3 --- Electrodiagnostic test --- p.10 / Chapter 1.1.6.4 --- Edrophonium test --- p.11 / Chapter 1.1.6.5 --- Imaging --- p.11 / Chapter 1.1.6.6 --- Pulmonary function test --- p.11 / Chapter 1.1.7 --- Treatment --- p.12 / Chapter 1.1.7.1 --- Medication --- p.12 / Chapter 1.1.7.2 --- Thymectomy --- p.12 / Chapter 1.1.7.3 --- Plasmapheresis and intravenous immunoglobulin --- p.13 / Chapter 1.2 --- Qiangji Jianli Fang --- p.14 / Chapter 1.2.1 --- Huang qi --- p.15 / Chapter 1.2.2 --- Dang shen --- p.16 / Chapter 1.2.3 --- Bai shu --- p.16 / Chapter 1.2.4 --- Dang gui --- p.17 / Chapter 1.2.5 --- Sheng ma --- p.17 / Chapter 1.2.6 --- Chai hu --- p.18 / Chapter 1.2.7 --- Chen pi --- p.18 / Chapter 1.2.8 --- Gan cao --- p.19 / Chapter 1.3 --- Metabolomics --- p.19 / Chapter 1.3.1 --- What’s metabolomics? --- p.20 / Chapter 1.3.1.1 --- Metabolites --- p.20 / Chapter 1.3.1.2 --- Metabolome --- p.21 / Chapter 1.3.1.3 --- Two terms: metabolomics and metabonomics --- p.21 / Chapter 1.3.2 --- How metabolomics works? --- p.22 / Chapter 1.3.2.1 --- Sample preparation --- p.22 / Chapter 1.3.2.1.1 --- Quenching --- p.23 / Chapter 1.3.2.1.2 --- Separating metabolites --- p.24 / Chapter 1.3.2.1.3 --- Sample concentration --- p.24 / Chapter 1.3.2.2 --- Analytical technologies (Sample analysis) --- p.25 / Chapter 1.3.2.3 --- Data analysis --- p.26 / Chapter 1.3.2.4 --- Database --- p.28 / Chapter 1.3.3 --- Why metabolomics? --- p.29 / Chapter 1.3.4 --- Metabolomics for human diseases --- p.30 / Chapter 1.3.5 --- Metabolomics for Traditional Chinese Medicine --- p.32 / Chapter 1.4 --- Objectives and significances of the present study --- p.34 / Chapter Chapter 2 --- Metabolic biomarkers of myasthenia gravis --- p.36 / Chapter 2.1 --- Introduction --- p.36 / Chapter 2.2 --- Materials and methods --- p.40 / Chapter 2.2.1 --- Chemicals --- p.40 / Chapter 2.2.2 --- Patients --- p.40 / Chapter 2.2.3 --- Volunteers --- p.42 / Chapter 2.2.4 --- Blood collection --- p.43 / Chapter 2.2.5 --- QC samples --- p.43 / Chapter 2.2.6 --- Sample processing --- p.43 / Chapter 2.2.7 --- Liquid chromatography-mass spectrometry --- p.44 / Chapter 2.2.8 --- Data analysis --- p.45 / Chapter 2.2.9 --- Metabolite identification --- p.45 / Chapter 2.3 --- Results --- p.46 / Chapter 2.3.1 --- Method validation --- p.46 / Chapter 2.3.2 --- An overall comparative analysis between 28 patients and 10 volunteers --- p.48 / Chapter 2.3.3 --- Classification of MG --- p.53 / Chapter 2.3.4 --- Comparative analysis of the metabolic changes in early- and late-stage MG patients respectively --- p.54 / Chapter 2.3.5 --- Biomarker identification --- p.56 / Chapter 2.4 --- Discussion --- p.58 / Chapter 2.5 --- Conclusion --- p.63 / Chapter Chapter 3 --- A novel diagnostic approach for myasthenia gravis --- p.64 / Chapter 3.1 --- Introduction --- p.64 / Chapter 3.2 --- Materials and methods --- p.68 / Chapter 3.2.1 --- Chemicals --- p.68 / Chapter 3.2.2 --- Patients and Volunteers --- p.69 / Chapter 3.2.2.1 --- Training set for establishment of diagnostic model --- p.69 / Chapter 3.2.2.2 --- Test set for evaluation of diagnostic model --- p.69 / Chapter 3.2.3 --- QC samples --- p.70 / Chapter 3.2.4 --- Sample processing --- p.71 / Chapter 3.2.5 --- Chromatography --- p.71 / Chapter 3.2.6 --- Mass spectrometry --- p.72 / Chapter 3.2.7 --- Data analysis --- p.72 / Chapter 3.3 --- Results --- p.72 / Chapter 3.3.1 --- Method validation --- p.73 / Chapter 3.3.2 --- Alterations in serum metabolic profile under MG --- p.74 / Chapter 3.3.3 --- Prediction of MG based on biomarkers --- p.74 / Chapter 3.3.4 --- Establishment of diagnostic model on the basis of metabolic profile --- p.77 / Chapter 3.3.5 --- Prediction of MG with diagnostic model --- p.79 / Chapter 3.4 --- Discussion --- p.80 / Chapter 3.5 --- Conclusion --- p.83 / Chapter Chapter 4 --- Qiangji Jianli Fang treatment for myasthenia gravis --- p.84 / Chapter 4.1 --- Introduction --- p.84 / Chapter 4.2 --- Materials and methods --- p.88 / Chapter 4.2.1 --- Chemicals --- p.88 / Chapter 4.2.2 --- Herbs --- p.88 / Chapter 4.2.3 --- Participants --- p.88 / Chapter 4.2.4 --- QC samples --- p.90 / Chapter 4.2.5 --- Sample processing --- p.90 / Chapter 4.2.6 --- Liquid chromatography-mass spectrometry --- p.90 / Chapter 4.2.7 --- Data analysis --- p.91 / Chapter 4.3 --- Results --- p.91 / Chapter 4.3.1 --- Method validation --- p.91 / Chapter 4.3.2 --- Symptomatic examination after QJF treatment --- p.92 / Chapter 4.3.3 --- Holistic metabolic responses to QJF treatment --- p.93 / Chapter 4.3.4 --- MG biomarkers changes after QJF treatment --- p.95 / Chapter 4.3.5 --- Drug-related biomarkers of QJF --- p.97 / Chapter 4.4 --- Discussion --- p.100 / Chapter 4.5 --- Conclusion --- p.103 / Chapter Chapter 5 --- Conclusions --- p.104 / Chapter Chapter 6 --- Perspectives --- p.107 / Chapter 6.1 --- Experimental autoimmune myasthenia gravis model --- p.107 / Chapter 6.2 --- Chemical composition of Qiangji Jianli Fang --- p.111 / References --- p.113 / Appendices --- p.130