基於世界上嚴重醫療短缺的原因，以及腦中風患者進行康復所需要的巨大經濟成本，採用輔助機器人裝置協助復健已被視為未來可能的新一代復健方法。這項研究的主要目的是開發中風復健療程所需的上肢輔助系統原型。 本研究的具體目標包括開發基於Microsoft Kinect和動態時間扭曲算法的運動模型採集系統，使用多傳感器構建反饋系統和感測器融合演算法的運用，以及幾種控制方法的比較， 例如極點配置，交叉耦合和增益調度方法。最後，整個控制系統由Arduino Mega2560嵌入式系統實現，運動數據處理和控制模擬則在MATLAB和Simulink上實現。

Due to the severe shortage of medical resources and huge financial burdens for families of stroke patients, assistive robotic devices and related technologies have gained much attention and been considered as an alternative approach of rehabilitation in the future.
The primary objective of this study was to develop a prototype of an upper limb assistive robotic device for rehabilitation activities. The specific aims of this study include the development of the motion acquisition system based on depth camera and temporal analysis algorithm, the investigation of motion sensing techniques by integrating multiple sensors with sensor fusion algorithms, and the development of adequate control scheme for a multi-axial robotic device with nonlinear factors and cross-coupling dynamics. In this study, the algorithms were developed and simulated using MATLAB and Simulink, and the control system was implemented using an embedded controller.

中文摘要----------------------------------------------------------Ⅰ
ABSTRACT---------------------------------------------------------Ⅱ
TABLE of CONTENTS-----------------------------------------------Ⅲ
LIST of FIGURES-------------------------------------------------Ⅵ
LIST of TABLES-------------------------------------------------Ⅷ
Chapter 1. Introduction------------------------------------------1
1.1 Background----------------------------------------------1
1.2 Objective-----------------------------------------------4
1.3 Structure of this document------------------------------8
Chapter 2. Literature Review------------------------------------10
2.1 History of stroke neural-rehabilitation----------------10
2.2 History and applications of robotic systems in stroke
rehabilitation-----------------------------------------12
2.3 Tele-rehabilitation of stroke patients-----------------15
2.4 Development of depth camera on human motion acquisition17
2.5 Applications of deep learning and CNNs on human action
recognition--------------------------------------------19
Chapter 3. Motion Acquisition-----------------------------------22
3.1 Introduction-------------------------------------------22
3.2 Introduction of Kinect and test environment set up-----23
3.3 Kinect program processing------------------------------27
3.4 The limitations of tested subjects and chosen motion
trajectories-------------------------------------------31
3.5 Motion acquisition results-----------------------------33
Chapter 4. Model Development------------------------------------41
4.1 Theory of dynamic time warping (DTW)-------------------42
4.2 Data normalization implementation----------------------46
4.3 Similarity analysis------------------------------------55
4.4 Extracting common trajectory---------------------------63
Chapter 5. Model Identification and Verification----------------65
5.1 Introduction-------------------------------------------65
5.2 Introduction of CNNs and its applications--------------66
5.3 Model identification using CNNs------------------------68
5.4 Model training implementation and results--------------70
5.5 Model verification using webcam------------------------73
Chapter 6. Experimental System Setup----------------------------80
6.1 Feedback sensor introduction---------------------------80
6.2 Sensor fusion------------------------------------------82
6.3 Sensor fusion result-----------------------------------86
6.4 Data storage environment system setup------------------91
6.5 Identification of robotic dynamics---------------------92
Chapter 7. Experimental System Setup----------------------------97
7.1 Introduction-------------------------------------------97
7.2 Control system design and simulation-------------------98
7.3 Control system implementation and result--------------103
7.4 Nonlinear improvement method one – Cross-Coupling-----113
7.5 Nonlinear improvement method two – Gain Scheduling----116
7.6 Conclusion--------------------------------------------121
Chapter 8. Conclusions and Future Work-------------------------123
References-----------------------------------------------------126

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