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研究生： 朱浩銓
Chu, Hao-Chuan
論文名稱： 基於人體運動之機器手臂路徑規劃與控制器合成
Path Planning and Controller Synthesis of Robotic Devices Based on Human Arm Motions
指導教授： 李昌駿
Lee, Chang-Chun
鄭宏銘
Cheng, Hung-Ming
口試委員: 林顯易
Lin, Sian-Yi
陳正義
Chen, Jheng-Yi
學位類別： 碩士
Master
系所名稱： 工學院 - 動力機械工程學系
Department of Power Mechanical Engineering
論文出版年： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 132
中文關鍵詞： 運動模型採集 、感測器融合 、多軸系統控制
外文關鍵詞： Motion-Acquisition, Sensor-Fusion, Control-of-Multi-Axial-Systems
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    • 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

    Admiraal, M. A., Kusters, M. J., & Gielen, S. C. (2004). Modeling kinematics and dynamics of human arm movements. Motor Control, 8(3), 312-338.
    Bishop, G., & Welch, G. (2001). An introduction to the kalman filter. Proc of SIGGRAPH, Course, 8(27599-23175), 41.
    Baccouche, M., Mamalet, F., Wolf, C., Garcia, C., & Baskurt, A. (2011, November). Sequential deep learning for human action recognition. In International Workshop on Human Behavior Understanding (pp. 29-39). Springer, Berlin, Heidelberg.
    Bilen, H., Fernando, B., Gavves, E., Vedaldi, A., & Gould, S. (2016). Dynamic image networks for action recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3034-3042).
    Chang, Y. J., Chen, S. F., & Huang, J. D. (2011). A Kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities. Research in developmental disabilities, 32(6), 2566-2570.
    Cheng, M. H., Guo, G., Banta, L.E. & Bakhoum, E. (2012, October). Identification of Arm Locomotion and Controller Synthesis for Assistive Robotic Systems. ICIC Express Letter, vol. 6, no. 10, pp. 2659-2665.
    Daly, J. J., Hogan, N., Perepezko, E. M., Krebs, H. I., Rogers, J. M., Goyal, K. S., ... & Ruff, R. L. (2005). Response to upper-limb robotics and functional neuromuscular stimulation following stroke. Journal of rehabilitation research & development, 42(6).
    Euston, M., Coote, P., Mahony, R., Kim, J., & Hamel, T. (2008, September). A complementary filter for attitude estimation of a fixed-wing UAV. In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 340-345). IEEE.
    Fang-I Hsieh and Hung-Yi Chiou (2014), "Stroke: Morbidity, Risk Factors, and Care in Taiwan," Journal of Stroke, vol. 16, no. 2, pp.59–64, 2014.
    Foggia, P., Saggese, A., Strisciuglio, N., & Vento, M. (2014, August). Exploiting the deep learning paradigm for recognizing human actions. In 2014 International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 93-98). IEEE.
    Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT press.
    Holden, M. K., Dyar, T. A., & Dayan-Cimadoro, L. (2007). Telerehabilitation using a virtual environment improves upper extremity function in patients with stroke. IEEE transactions on neural systems and rehabilitation engineering, 15(1), 36-42.
    Ijjina, E. P., & Chalavadi, K. M. (2016). Human action recognition using genetic algorithms and convolutional neural networks. Pattern recognition, 59, 199-212.
    Ji, S., Xu, W., Yang, M., & Yu, K. (2013). 3D convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence, 35(1), 221-231.
    Jarrassé, N., Proietti, T., Crocher, V., Robertson, J., Sahbani, A., Morel, G., & Roby-Brami, A. (2014). Robotic exoskeletons: a perspective for the rehabilitation of arm coordination in stroke patients. Frontiers in human neuroscience, 8, 947.
    Jiang Lei. (2015). Design and Development of a Twisted String Exoskeleton Robot for the Upper Limb. Dissertation submitted to the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University.
    Krebs, H. I., Ferraro, M., Buerger, S. P., Newbery, M. J., Makiyama, A., Sandmann, M., ... & Hogan, N. (2004). Rehabilitation robotics: pilot trial of a spatial extension for MIT-Manus. Journal of NeuroEngineering and Rehabilitation, 1(1), 5.
    Kahn, L. E., Lum, P. S., Rymer, W. Z., & Reinkensmeyer, D. J. (2014). Robot-assisted movement training for the stroke-impaired arm: Does it matter what the robot does ?.
    Lum, P. S., Burgar, C. G., Shor, P. C., Majmundar, M., & Van der Loos, M. (2002). Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke. Archives of physical medicine and rehabilitation, 83(7), 952-959.
    Lum, P. S., Burgar, C. G., Van der Loos, M., Shor, P. C., Majmundar, M., & Yap, R. (2006). MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: A follow-up study. Journal of rehabilitation research & development, 43(5), 631-643.
    Lo, A. C., Guarino, P. D., Richards, L. G., Haselkorn, J. K., Wittenberg, G. F., Federman, D. G., ... & Bever Jr, C. T. (2010). Robot-assisted therapy for long-term upper-limb impairment after stroke. New England Journal of Medicine, 362(19), 1772-1783.
    LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
    Jiang Lei. (2015). Design and Development of a Twisted String Exoskeleton Robot for the Upper Limb. Dissertation submitted to the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University.
    Mulder, T. (1991). A process-oriented model of human motor behavior: toward a theory-based rehabilitation approach. Physical therapy, 71(2), 157-164.
    Masiero, S., Celia, A., Rosati, G., & Armani, M. (2007). Robotic-assisted rehabilitation of the upper limb after acute stroke. Archives of physical medicine and rehabilitation, 88(2), 142-149.
    Microsoft Developer Network (2018). Retrieved October, 23 2018. From https://msdn.microsoft.com/zh-tw/hh367958
    Nef, T., & Riener, R. (2005, June). ARMin-design of a novel arm rehabilitation robot. In Rehabilitation Robotics, 2005. ICORR 2005. 9th International Conference on (pp. 57-60). IEEE.
    Nicol Korner-Bitensky (2016, August). "Fugl-Meyer Assessment of Sensorimotor Recovery After Stroke (FMA)". In Canadian Partnership for Stroke Recovery.
    Oden, R. (1918). Systematic therapeutic exercises in the management of the paralyses in hemiplegia. Journal of the American Medical Association, 70(12), 828-833.
    Pulvermüller, F., Neininger, B., Elbert, T., Mohr, B., Rockstroh, B., Koebbel, P., & Taub, E. (2001). Constraint-induced therapy of chronic aphasia after stroke. Stroke, 32(7), 1621-1626.
    Paci, M. (2003). Physiotherapy based on the Bobath concept for adults with post-stroke hemiplegia: a review of effectiveness studies. Journal of rehabilitation medicine, 35(1), 2-7.
    Prange, G. B., Jannink, M. J. A., Groothuis-Oudshoorn, C. G. M., Hermens, H. J., & Ijzerman, M. J. (2009). Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. Journal of rehabilitation research and development, 43(2), 171-184.
    Ren, Y., Park, H. S., & Zhang, L. Q. (2009, June). Developing a whole-arm exoskeleton robot with hand opening and closing mechanism for upper limb stroke rehabilitation. In Rehabilitation Robotics, 2009. ICORR 2009. IEEE International Conference on (pp. 761-765). IEEE.
    Rachel Nall (2018). "History of Stroke," Healthline, from https://www.healthline.com/health/stroke/history-of-stroke#history-of-treatments.
    Sanchez, R. J., Wolbrecht, E., Smith, R., Liu, J., Rao, S., Cramer, S., ... & Reinkensmeyer, D. J. (2005, June). A pneumatic robot for re-training arm movement after stroke: Rationale and mechanical design. In Rehabilitation Robotics, 2005. ICORR 2005. 9th International Conference on (pp. 500-504). IEEE.
    Saini, S., Rambli, D. R. A., Sulaiman, S., Zakaria, M. N., & Shukri, S. R. M. (2012, June). A low-cost game framework for a home-based stroke rehabilitation system. In Computer & Information Science (ICCIS), 2012 International Conference on (Vol. 1, pp. 55-60). IEEE.
    Su, C. J., Chiang, C. Y., & Huang, J. Y. (2014). Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic. Applied Soft Computing, 22, 652-666.
    Suthaharan, S. (2016). Machine learning models and algorithms for big data classification. Integr. Ser. Inf. Syst, 36, 1-12.
    Twitchell, T. E. (1951). The restoration of motor function following hemiplegia in man. Brain, 74(4), 443-480.
    Zhou, H., & Hu, H. (2008). Human motion tracking for rehabilitation—A survey. Biomedical Signal Processing and Control, 3(1), 1-18.
    Zhou Yuan (2015). “Rehabilitation robot description”, Chinese Journal of Rehabilitation Medicine. Retrieved Apr.2015. From http://www.rehabi.com.cn/ch/reader/create_pdf.aspx?file_no=201504024&year_id=2015&quarter_id=4&falg=1

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