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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
| dc.contributor.author | Yuan-Kai Chang | en |
| dc.contributor.author | 張原愷 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:45:52Z | - |
| dc.date.copyright | 2022-08-19 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-08-10 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85136 | - |
| dc.description.abstract | 中風是一種突發性腦血管疾病，造成患者功能失調。臨床研究顯示，若中風患者進行長期的復健治療，能夠有效幫助患者恢復原有的運動功能。復健機器人不僅可以提供患者更穩定的復健療程、減輕治療師的負擔，也可以透過機器上的感測器獲得客觀資料作為評分的依據。此外，在復健療程的過程中，患者可能會發生失能性痙攣的症狀，此時機器人若持續移動，反而會對患者造成二次傷害。因此，將偵測患者痙攣狀態的方法納入基於外骨骼的復健控制策略有其必要性。 在相關的文獻中，對於肌力不足的患者，使用交互作用扭矩觀測器或肌電訊號的控制策略來進行復健療程是有較高的難度。本研究針對上肢復健機器人，提出一個結合肌電訊號以及交互作用扭矩觀測器的控制策略。首先，本研究提出的運動意圖辨識模型會透過表面肌電訊號預測使用者上肢的運動方向，並結合先前提出的交互作用扭矩觀測器獲得使用者運動意圖。接著，根據主動肌與拮抗肌的肌電訊號來評估患者運動過程中的痙攣狀態是否異常。最後，本研究提出一個整合運動意圖以及痙攣狀態異常評估的控制策略。 本研究提出的方法在四位健康受試者上做測試，其結果顯示，結合兩種運動意圖的控制方法可以提升主動控制的表現。此外，根據主動肌與拮抗肌之肌電訊號所計算出來的指標也可以有效區分痙攣狀態的異常。 | zh_TW |
| dc.description.abstract | Stroke is a sudden cerebrovascular disease resulting in dysfunction. Clinical research shows that if stroke patients undergo long-term rehabilitation treatment, it can effectively help patients restore their original motor function. Rehabilitation robots can provide patients with more stable rehabilitation therapy, reduce the burden of therapists, and obtain objective data through the sensor on the machine as the basis of scoring. In addition, during the rehabilitation therapy, the patient may have disabling spasticity. If the robot continues to move, it will cause secondary injury to the patient. Therefore, it is necessary to incorporate detecting state of spasticity into the rehabilitation control strategy based on the exoskeleton. In the related literature, it is difficult for patients with muscle weakness to carry out the rehabilitation therapy with the interactive torque observer or the electromyography signal. In this research, a control strategy combining electromyography signal and interactive torque observer is developed for the upper limb rehabilitation robot. Firstly, the motion intention recognition model proposed in this research will predict the motion direction of the user’s upper limb through the surface electromyography signal and obtain the user’s motion intention combined with the previously proposed interactive torque observer. Then, a mechanism will evaluate the electromyography signals from antagonistic muscle pairs to prevent the disabling spasticity. Finally, this research proposes a control strategy integrating exercise intention and muscle co-contraction evaluation. The method proposed in this research is verified through experiments with four healthy subjects. The results show that the combination of two motion intentions can improve the performance of the active control. In addition, the indexes calculated from the antagonistic muscle pairs’ electromyographic signals can also effectively identify the abnormalities of the state of spasticity. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:45:52Z (GMT). No. of bitstreams: 1 U0001-1008202212134600.pdf: 11427532 bytes, checksum: d625ec88684079a5dbc04e68398e559c (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 致謝 i 中文摘要 ii ABSTRACT iii CONTENTS v TABLE OF ACRONYMS viii LIST OF FIGURES ix LIST OF TABLES xi Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Literature Review 4 1.2.1 Robotics in Rehabilitation 4 1.2.2 Human Intention Extraction for Rehabilitation Therapy 5 1.2.3 Spasticity Evaluation 7 1.3 Contribution 8 1.4 Thesis Organization 9 Chapter 2 Preliminaries 10 2.1 Upper Limb Rehabilitation Robot NTUH-II 10 2.1.1 Hardware Specification of NTUH-II 10 2.1.2 Safety Issue of NTUH-II 15 2.2 sEMG Instrument 16 2.3 Forward Kinematics 17 2.4 Jacobian Matrix in Robotics 19 2.4.1 Angular Velocity 21 2.4.2 Linear Velocity 22 2.4.3 Application to NTUH-II 22 2.5 Robot Dynamics of NTUH-II 22 2.6 Kalman Filter 26 2.7 Machine Learning 29 2.7.1 Convolutional Neural Network 29 2.7.2 Graph Convolution Network 31 Chapter 3 Design of Motion Intention Based Control 34 3.1 Overview of System 34 3.2 Data Acquisition and Signal Preprocessing 36 3.2.1 Sensor Placement 37 3.2.2 General Signal Preprocessing 39 3.2.3 Crosstalk Filter 41 3.2.4 Task-Specific Signal Preprocessing 45 3.3 Human Motion Intention Detection 48 3.3.1 Data Collection for Motion Intention Recognition 50 3.3.2 Graph Convolution Network Model 52 3.3.3 Kalman Filter based Interactive Torque Observer 56 3.3.4 Intention Integration Algorithm and Reference Trajectory 60 3.4 Prevention Mechanism for Disabling Spasticity 63 3.5 Stability Analysis 66 Chapter 4 Experiments and Results 70 4.1 Experiment Setup 70 4.1.1 Experiment for Signal Preprocessing 71 4.1.2 Experiment for Motion Intention Recognition Model 71 4.1.3 Experiment for Spasticity Evaluation 73 4.1.4 Experiment for Control Strategy 73 4.2 Experiment Result 76 4.2.1 Performance of Signal Preprocessing 76 4.2.2 Performance of Motion Intention Recognition Model 78 4.2.3 Feasibility of Spasticity Evaluation 82 4.2.4 Performance of Control Strategy 86 Chapter 5 Conclusion 92 REFERENCE 94 | |
| dc.language.iso | en | |
| dc.subject | NTUH-II | zh_TW |
| dc.subject | 復健機器人 | zh_TW |
| dc.subject | 表面肌電訊號 | zh_TW |
| dc.subject | 運動意圖辨識控制 | zh_TW |
| dc.subject | rehabilitation robot | en |
| dc.subject | surface electromyography | en |
| dc.subject | pattern recognition control | en |
| dc.subject | NTUH-II | en |
| dc.title | 以交互作用扭矩觀測器輔助表面肌電圖動作辨識控制應用於上肢復健外骨骼機器人 | zh_TW |
| dc.title | sEMG Pattern Recognition Control Assisted with Interactive Torque Observer for Upper Limb Rehabilitation Robot | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 賴金鑫(Jin-Shin Lai),陳文翔(Wen-Shiang Chen),劉浩澧(Hao-Li Liu),陳政維(Cheng-Wei Chen) | |
| dc.subject.keyword | 復健機器人,表面肌電訊號,運動意圖辨識控制,NTUH-II, | zh_TW |
| dc.subject.keyword | rehabilitation robot,surface electromyography,pattern recognition control,NTUH-II, | en |
| dc.relation.page | 101 | |
| dc.identifier.doi | 10.6342/NTU202202245 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-08-11 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2025-08-10 | - |
| 顯示於系所單位： | 電機工程學系 | |
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