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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃漢邦(Han-Pang Huang) | |
dc.contributor.author | Tzu-Hao Huang | en |
dc.contributor.author | 黃子豪 | zh_TW |
dc.date.accessioned | 2021-06-16T10:17:34Z | - |
dc.date.available | 2015-08-23 | |
dc.date.copyright | 2013-08-23 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-08-17 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60413 | - |
dc.description.abstract | 隨著科技的發展,使得機器人領域有了大的進展。不久的將來,機器人將頻繁出現在人類生活環境中,像是學校、醫院、辦公大樓、博物館、以及一般家庭等等區域。為了要使得機器人更容易可以與人類互動,機器人必須理解環境中的人類的意圖並根據所得知之訊息做出相對應的反應。
因此本論文目的旨在發展藉由人類肌電與腦波訊號,使機器人能夠偵測人類意圖的方法,並設計具有內在安全性之彈性機構與變剛性機構,使得人類與機器在物理上的互動可以更加的安全,並根據生物訊號之特性來設計相對應之復健與支持運動之控制方法。對於人類意圖的估測,提出了使用幾種根據EMG與EEG特性而設計之架構。EMG上面,主要以肌肉模型、動態模型並以Graphical Model融合不同估測結果。EEG則提出了使用ICA-MKL之架構來分類P300之腦電位訊號。而在機構之設計上則提出了容易組裝與輕便簡易之可反驅動彈簧機構與複雜可變換剛性之耦合連續剛性彈性機構以因應不同任務之需求兩種架構,使得整個系統擁有被動之安全性。在智慧型控制層次上,為了建構能針對各式各樣任務之控制法則,本論文進而發展了容易使用且整合EMG控制與零阻抗控制之混合控制與以人類EMG和順應之特性而發展之虛擬EMG順應性控制等概念。 最後本論文展示藉由結合人類意圖偵測、安全機構設計與智慧型控制,將應用拓展至手軸關節復健與支持運動、膝關節支持運動與人類行走與爬樓梯之支持運動上,展示了機器未來輔助人類的可能性。 | zh_TW |
dc.description.abstract | The robot technologies have a lot of progress recently. In the near future, robots will appear much more frequently in daily human life, such as in schools, hospitals, offices, museums, and the home. In order to assist and interact with humans, the robots need to understand the humans’ intentions and respond accordingly.
Therefore, the aim of this dissertation is to develop human intention estimation methods using electromyography (EMG), electroencephalography (EEG), and dynamic information; and to design an elastic and variable stiffness mechanism with intrinsic safety, and advanced control for assistive and rehabilitation exercises. We use a muscle model to find the relationship between EMG and torque. Moreover, an exoskeleton graphical model is proposed to merge the EMG signal and dynamic information, and enhance the stabilization of the robot-human system. For extremely weak patients, independent component analysis-multiple kernel learning (ICA-MKL) is also proposed to increase the classification accuracy of the EEG human intention estimation. In order to satisfy the demand of the assistive and rehabilitation exercises, two different kinds of mechanism approaches are proposed. Backdrivable torsion spring actuators (BTSA) are recommended because they are light weight, compact, and mainly used in assistive control. Considering their performance and safety, variable stiffness and coupled elastic actuation are designed to adaptively change for different tasks. Finally, intelligent and safety controls are also developed to help humans in assistive exercises, rehabilitation, and walking, and guarantee safety under variable environments. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T10:17:34Z (GMT). No. of bitstreams: 1 ntu-102-D95522013-1.pdf: 7485539 bytes, checksum: 941975e136817111697fe937c8b7c6c2 (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 摘要 i
Abstract iii CHAPTER 1. Introduction 1 1.1. Objective and Motivation 1 1.2. Robots and Human Robot Interaction 3 1.3. Biofeedback Control for Rehabilitation and Assistive Robots 3 1.4. Human Intention Estimation and Classification 4 1.5. The Organization of the Dissertation 6 CHAPTER 2. Design of Series Elastic Mechanisms for Safety 9 2.1. Introduction 9 2.2. Mechanism of Adaptive Coupled Elastic Actuation 12 2.2.1. Design Concept 12 2.2.2. Mechanism Design 14 2.2.3. System Properties 17 2.3. Mechanism of Backdrivable Torsion Spring Actuation 19 2.4. Mechanism of Coupled Continuous-State Elastic Actuation 22 2.4.1. CCEA Design Concept 22 2.4.2. Practical CCEA Design and Working Principle 25 2.5. Summary 28 CHAPTER 3. Biofeedback Signal Processing 29 3.1. Introduction 29 3.2. EMG signal processing and Kalman filter for EMG feature extraction 31 3.3. EEG signal processing for P300 Detection 34 3.3.1. Experimental Setup and EEG signal processing 36 3.3.2. Testing of P300 and non-P300 Classifications 36 3.3.3. Testing of the Online Brain-Controlled Rehabilitation System 37 3.4. Independent component analysis for EEG feature extraction 37 3.4.1. Independent Component Analysis 38 3.5. Summary 40 CHAPTER 4. Human Intention Estimation 41 4.1. Introduction 41 4.2. Muscle Model for Human Intention Estimation 41 4.2.1. Binary Intention Estimation 41 4.2.2. Linear Regression for Continuous Intention Estimation 42 4.2.3. Muscle Model with Linear Regression 42 4.2.4. Result of Binary Intention Estimation 45 4.2.5. Results of Linear Regression for Continuous Intentions 46 4.2.6. Results of Muscle Mode with Linear Regression 47 4.2.7. Comparison between Kalman Filter and Muscle Model 48 4.3. Online Dynamics and EMG Parameters Estimation 50 4.3.1. The Dynamics Model and the EMG Model 52 4.3.2. Offline System Identifications 53 4.3.3. Self-Learning Scheme and Sliding Mode Admittance Control 54 4.4. Human Joint Torque Estimation by the Quasi-Static Human Model 59 4.4.1. The Quasi-Static Human Model 59 4.4.2. The Single Support Phase of Human Model 61 4.4.3. The Double Support Phase of Human Model 62 4.4.4. The Swing Phase of Human Model 64 4.5. Graphical Model for Exoskeleton Human Intention Estimation 65 4.5.1. Bayesian Networks and Graphical Model 65 4.5.2. Gaussian Process Regression 66 4.5.3. Bayesian human intention estimator 68 4.5.4. Human Intention Estimation by Bayesian Reasoning 72 4.5.5. The Biosignal Model 75 4.5.6. The Inverse Dynamics Model 76 4.5.7. The Exogenous Disturbance Model 76 4.5.8. Results of the Biosignal Model 79 4.5.9. Results of Inverse Dynamics Model 80 4.5.10. Results of the Exogenous Disturbance Model 81 4.6. SVM and MKL in the EEG Classification 81 4.6.1. Support Vector Machine (SVM) 81 4.6.2. Multiple Kernel Learning 83 4.6.3. Classification Results of P300 and Non-P300 84 4.6.4. Experimental Results of BCRS with KNN, SVM, and MKL 88 4.7. Multiple Kernel Learning with ICA for EEG Classification 89 4.7.1. Automatic Feature Extraction by ICA-MKL 89 4.7.2. Results on BCI 2008 Data 2a 91 4.8. Summary 96 CHAPTER 5. Control for Rehabilitation and Assistive Exercise 97 5.1. Introduction 97 5.2. Control for Active-Passive Elbow Rehabilitation 98 5.2.1. Performance Evaluations 98 5.3. Human Intention Amplifier for Elbow Assistive Control 102 5.3.1. A Human-Robot Interaction Model 103 5.3.2. Open-Loop Transfer function for Safety Performance 104 5.3.3. Closed-Loop Transfer function for Assistive Exercises 104 5.4. Hybrid Control for Automatic Assistive Control 107 5.4.1. A Simple Human-Robot Interaction Model 107 5.4.2. Zero-Impedance Control 108 5.4.3. Direct EMG Biofeedback Control 109 5.4.4. Hybrid Control: Support-When-Necessary 110 5.4.5. Stability 111 5.4.6. Simulations 114 5.5. Virtual EMG Admittance Sliding Mode Control 117 5.6. Assistive Control with Bayesian Estimator 120 5.7. Walking Assistive Control and Walking Stabilizer 121 5.8. Summary 125 CHAPTER 6. Applications and Experiments 127 6.1. Introduction 127 6.2. Active-Passive Elbow Rehabilitation Robot 127 6.2.1. Sensors and Control System 128 6.2.2. Control Architecture and Experiments 130 6.3. Brain Controlled Rehabilitation System 136 6.3.1. The system architecture of BCRS 136 6.3.2. Experimental Results of BCRS 140 6.4. Elbow Assistive Robot by Binary intention Estimation 143 6.5. Knee Assistive Robot in Three Kinds of Approaches 148 6.5.1. Knee assistive by Hybrid Control 149 6.5.2. Online knee assistive during human walking 154 6.5.3. Knee assistive by Bayesian Estimator 157 6.6. Applications in Walking Assistive Robot 168 6.7. Summary 172 CHAPTER 7. Conclusions and future works 173 7.1. Summary 173 7.1.1. Safety Mechanism Design 173 7.1.2. Human Intention Estimation 174 7.1.3. Rehabilitation and Assistive Control 174 7.2. Future Works 175 7.2.1. Adaptive Joint Design 175 7.2.2. EMG Admittance Identification and Control 178 7.3. Conclusions 178 References 180 Biography 193 | |
dc.language.iso | en | |
dc.title | 使用生物訊號回授與彈性機構之復健與支持運動控制 | zh_TW |
dc.title | Rehabilitation and Assistive Exercises using Biofeedback Signals and Series Elastic Mechanism | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 呂東武(Tung-Wu Lu),林其禹(Chyi-Yeu Lin),曾清秀(Ching-Shiow Tseng),顏炳郎(Ping-Lang Yen),劉益宏(Yi-Hung Liu) | |
dc.subject.keyword | 物理人機互動,智慧型控制,彈性機構設計,輔助與復健機器人,圖形模型,動態與肌電參數估測, | zh_TW |
dc.subject.keyword | human robot interaction,elastic actuation,assistive and rehabilitation robot,intelligent control,human intention estimation, | en |
dc.relation.page | 195 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-08-17 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
顯示於系所單位: | 機械工程學系 |
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