博碩士論文 109521080 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:105 、訪客IP:3.138.119.142
姓名 林宗諭(Zong-Yu Lin)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 基於肌電訊號控制之氣動式上肢外骨骼系統
(Implementation of an EMG-controlled Upper- Limb Exoskeleton Robot Driven by Pneumatic Muscle Actuators)
相關論文
★ 使用梳狀濾波器於相位編碼之穩態視覺誘發電位腦波人機介面★ 應用電激發光元件於穩態視覺誘發電位之腦波人機介面判斷
★ 智慧型手機之即時生理顯示裝置研製★ 多頻相位編碼之閃光視覺誘發電位驅動大腦人機介面
★ 以經驗模態分解法分析穩態視覺誘發電位之大腦人機界面★ 利用經驗模態分解法萃取聽覺誘發腦磁波訊號
★ 明暗閃爍視覺誘發電位於遙控器之應用★ 使用整體經驗模態分解法進行穩態視覺誘發電位腦波遙控車即時控制
★ 使用模糊理論於穩態視覺誘發之腦波人機介面判斷★ 利用正向模型設計空間濾波器應用於視覺誘發電位之大腦人機介面之雜訊消除
★ 智慧型心電圖遠端監控系統★ 使用隱馬可夫模型於穩態視覺誘發之腦波人機介面判斷 與其腦波控制遙控車應用
★ 使用類神經網路於肢體肌電訊號進行人體關節角度預測★ 使用等階集合法與影像不均勻度修正於手指靜脈血管影像切割
★ 應用小波編碼於多通道生理訊號傳輸★ 結合高斯混合模型與最大期望值方法於相位編碼視覺腦波人機介面之目標偵測
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2028-1-1以後開放)
摘要(中) 本研究提出以EMG訊號即時操控單軸上肢氣動式外骨骼,EMG訊號主要為肌肉收縮產生的生理訊號,將肌電訊號輸入至MATLAB/Simulink軟體中進行訊號處理,將其進行整流平均(Derive Average Rectified,ARV)運算,結合PID控制器以及EMG訊號轉換類比電壓閥值判斷法,進而讓使用者以EMG訊號驅動氣動式外骨骼,讓使用者能將EMG訊號維持在出力水平以及將外骨骼角度維持在平衡點上。本研究分別在6公斤與9公斤的負重實驗上,設定受試者出力為3kg,誤差分別為21%、8.7%,證明本系統之成效。
摘要(英) This thesis is aimed to using Electromyography (EMG) for real-time control of a One-DOF upper-limb exoskeleton robot. The EMG signal is a physiological signal generated by muscle contraction.EMG signals were input into MATLAB/Simulink software for signal processing. We perform Derive Average Rectified (ARV) operation on it. Combining PID controller and EMG signal conversion analog voltage threshold judgment method to drive the pneumatic exoskeleton.The user can maintain the EMG signal at the output level and exoskeleton can maintain the angle at the Equilibrium point.In the 6kg and 9kg weight-bearing experiments, the subject′s effort was set to 3kg, and the errors were 21% and 8.7%, respectively, to prove the effectiveness of this system.
關鍵字(中) ★ 上肢外骨骼
★ 氣動式
★ 肌電訊號
關鍵字(英)
論文目次 中文摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 xi
第一章 緒論 1
1-1 研究動機與目的 1
1-2 文獻探討 3
1-3 論文章節結構 5
第二章 原理介紹 6
2-1 人體骨骼構造 6
2-2 EMG訊號介紹 7
2-2-2 EMG訊號量測方法與位置 8
2-2-3 腦波機硬體架構 9
2-3 外骨骼機構設計 11
2-4 氣動元件介紹 12
2-4-1 Mckibben氣動肌腱 12
2-4-2 氣動肌腱模型建模 13
2-4-3 外骨骼機器人組裝及穿戴 14
2-4-4 比例調壓閥(VPPM) 15
2-4-5 電磁閥(Solenoid valve) 16
第三章 研究設計與方法 18
3-1 EMG訊號處理 18
3-2 外骨骼機器手臂氣壓系統迴路 21
3-3 外骨骼機器手臂硬體設計 22
3-4 外骨骼機器手臂系統模擬 26
3-4-1 模擬系統參數調整 33
3-5 實際外骨骼機器手臂系統 47
3-6 實驗方法 51
3-6-1 目標EMG訊號分析實驗 51
3-6-2 外骨骼機器手臂規格測試 52
3-6-3 外骨骼輔助系統實驗 53
第四章 結果與討論 55
4-1 目標EMG訊號實驗結果 55
4-2 外骨骼機器手臂規格測試結果 62
4-3 外骨骼輔助系統驗證結果 68
4-3-1 外骨骼輔助誤差 75
4-4 實驗結果討論 78
第五章 結論與未來展望 80
參考文獻 81

參考文獻 [1] Hitzl, W., Stamm, T., Kloppenburg, M. et al. Projected number of osteoarthritis patients in Austria for the next decades – quantifying the necessity of treatment and prevention strategies in Europe. BMC Musculoskelet Disord 23, 133 (2022).
[2] Egle Pirie (July 08, 2022) Arm muscles Retrieved October 18, 2022 from https://www.kenhub.com/en/library/anatomy/arm-muscles
[3] Rowena Nichols (July 11, 2012) muscle anatomy Retrieved October 18, 2022 from
https://www.pinterest.com/pin/431290101796810912/
[4] East Dragon Precise Transmission Co.,LTD Retrieved October 25, 2013 from http://www.edpt.com.tw/big5/upfiles/shop/files/CS
[5] FESTO Didactic GmbH & Co. KG, Festo Retrieved 8 October 2022 Corporation https://www.festo.com/cat/en-us_us/data/doc_enus
[6] G. Andrikopoulos, G. Nikolakopoulos and S. Manesis, "A Survey on applications of Pneumatic Artificial Muscles," 2011 19th Mediterranean Conference on Control & Automation (MED), 2011, pp. 1439-1446, doi: 10.1109/MED.2011.5982983.
[7]Micera S, Carpaneto J, Raspopovic S. Control of hand prostheses using peripheral information. IEEE Rev Biomed Eng. 2010;3:48-68. doi: 10.1109/RBME.2010.2085429. PMID: 22275201.
[8]Nazmi N, Abdul Rahman MA, Yamamoto S, Ahmad SA, Zamzuri H, Mazlan SA. A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions. Sensors (Basel). 2016 Aug 17;16(8):1304. doi: 10.3390/s16081304. PMID: 27548165; PMCID: PMC5017469.
[ 9]https://zhuanlan.zhihu.com/p/35679252
[10]R. Kako, N. Saito, D. Furukawa and T. Satoh, "Muscle activity control using an EMG feedback based pneumatic artificial muscle power-assist device," IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, 2020, pp. 644-649, doi: 10.1109/IECON43393.2020.9254737.
[11]T. Lenzi, S. M. M. De Rossi, N. Vitiello and M. C. Carrozza, "Intention-Based EMG Control for Powered Exoskeletons," in IEEE Transactions on Biomedical Engineering, vol. 59, no. 8, pp. 2180-2190, Aug. 2012, doi: 10.1109/TBME.2012.2198821.
[12]Peternel L, Noda T, Petrič T, Ude A, Morimoto J, et al. (2016) Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation. PLOS ONE 11(2): e0148942.
[13] Cai S, Chen Y, Huang S, Wu Y, Zheng H, Li X and Xie L (2019) SVM-Based Classification of sEMG Signals for Upper-Limb Self-Rehabilitation Training. Front. Neurorobot. 13:31. doi: 10.3389/fnbot.2019.00031
[14]M. R. Ahsan, M. I. Ibrahimy and O. O. Khalifa, "Hand motion detection from EMG signals by using ANN based classifier for human computer interaction," 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization, 2011, pp. 1-6, doi: 10.1109/ICMSAO.2011.5775536.
[15]E. Cavallaro, J. Rosen, J. C. Perry, S. Burns and B. Hannaford, "Hill-Based Model as a Myoprocessor for a Neural Controlled Powered Exoskeleton Arm - Parameters Optimization," Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005, pp. 4514-4519, doi: 10.1109/ROBOT.2005.1570815.
[16]Y.-P. Sun, S.-C. Chen, Y.-C. Liang, and L.-N. Wu, “Design of a bionic-inspired exoskeleton robot for lower limb assist,” Journal of Vibroengineering, Vol. 18, No. 8, pp. 5452–5461, Dec. 2016, https://doi.org/10.21595/jve.2016.17427
[17]王宜楷 (2009)。《類比控制電路設計與控制實驗-第二版》。 臺北:全華圖書。
指導教授 李柏磊 審核日期 2023-2-1
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明