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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93279完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張秉純 | zh_TW |
| dc.contributor.advisor | Biing-Chwen Chang | en |
| dc.contributor.author | 吳恩奇 | zh_TW |
| dc.contributor.author | En-Chi Wu | en |
| dc.date.accessioned | 2024-07-23T16:39:09Z | - |
| dc.date.available | 2024-07-24 | - |
| dc.date.copyright | 2024-07-23 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-22 | - |
| dc.identifier.citation | [1] R. A. Bonney and E. N. Corlett, “Head posture and loading of the cervical spine,” Applied Ergonomics, vol. 33, no. 5, pp. 415–417, Sep. 2002.
[2] K. Murata et al., “Relationship between cervical and global sagittal balance in patients with dropped head syndrome,” Eur Spine J, vol. 29, no. 3, pp. 413–419, Mar. 2020. [3] P. Lingampally and A. Selvakumar, “A kinematic and workspace analysis of a parallel rehabilitation device for head-neck injured patients,” FME Transactions, vol. 47, no. 3, pp. 405–411, 2019, doi: 10.5937/fmet1903405L. [4] C. Thalman and P. Artemiadis, “A review of soft wearable robots that provide active assistance: Trends, common actuation methods, fabrication, and applications,” Wearable Technol., vol. 1, p. e3, 2020, doi: 10.1017/wtc.2020.4. [5] B. Zhong, K. Guo, H. Yu, and M. Zhang, “Toward Gait Symmetry Enhancement via a Cable-Driven Exoskeleton Powered by Series Elastic Actuators,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 786–793, Apr. 2022. [6] Haohan Zhang; Sunil K. Agrawal, "Kinematic Design of a Dynamic Brace for Measurement of Head/Neck Motion", Proc. IEEE Int. Conf. Robot. Autom., pp. 1428-1435, 2017. [7] R. Beira et al., "Design of the robot-cub (iCub) head", Proc. IEEE Int. Conf. Robot. Autom., pp. 94-100, 2006. [8] H. Zhang, B.-C. Chang, Y.-J. Rue and S. K. Agrawal, "Using the motion of the head-neck as a joystick for orientation control", IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 2, pp. 236-243, 2019. [9] H. Zhang, K. Albee and S. K. Agrawal, "A spring-loaded compliant neck brace with adjustable supports", Mechanism and Machine Theory, vol. 125, pp. 34-44, 2018. [10] H. Zhang and S. K. Agrawal, "An active neck brace controlled by a joystick to assist head motion", IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 37-43, 2017. [11] Luo-Xian Xiang,“Artificial Intelligence for Recognition and Classification of Different Sources of Coarse Aggregates ,”NTU Theses and Dissertation Repository, 2023. [12] H. Zhang, A novel robotic platform to assist train and study head-neck movement, 2019. [13] H. Zhang, V. Santamaria and S. Agrawal, "Applying force perturbations using a wearable robotic neck brace", 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4197-4202. [14] A. Vabalas, E. Gowen, E. Poliakoff, and A. J. Casson, “Machine learning algorithm validation with a limited sample size,” PLOS ONE, vol. 14, no. 11, p. e0224365, Nov. 2019. [15] H. Zhang, H. Ma, J. Ren, D. Shi, and W. Zhang, “Design and Kinematic Analysis of a Cable-driven Exoskeleton for Cervical Rehabilitation,” in 2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA), Aug. 2023, pp. 1495–1500. doi: 10.1109/ICIEA58696.2023.10241451. [16] Z. Li, X. Guan, K. Zou, and C. Xu, “Estimation of Knee Movement from Surface EMG Using Random Forest with Principal Component Analysis,” Electronics, vol. 9, no. 1, Art. no. 1, Jan. 2020, doi: 10.3390/electronics9010043. [17] J. Coker, H. Chen, M. C. Schall, S. Gallagher, and M. Zabala, “EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee,” Sensors, vol. 21, no. 11, Art. no. 11, Jan. 2021, doi: 10.3390/s21113622. [18] Y. Lu, H. Wang, B. Zhou, C. Wei, and S. Xu, “Continuous and simultaneous estimation of lower limb multi-joint angles from sEMG signals based on stacked convolutional and LSTM models,” Expert Systems with Applications, vol. 203, p. 117340, Oct. 2022, doi: 10.1016/j.eswa.2022.117340. [19] K. Chin, T. Hellebrekers, and C. Majidi, “Machine Learning for Soft Robotic Sensing and Control,” Advanced Intelligent Systems, vol. 2, no. 6, p. 1900171, 2020, doi: 10.1002/aisy.201900171. [20] A. N. Vasavada, J. Danaraj, and G. P. Siegmund, “Head and neck anthropometry, vertebral geometry and neck strength in height-matched men and women,” Journal of Biomechanics, vol. 41, no. 1, pp. 114–121, 2008, doi: 10.1016/j.jbiomech.2007.07.007. [21] W. Lee et al., “A 3D anthropometric sizing analysis system based on North American CAESAR 3D scan data for design of head wearable products,” Computers & Industrial Engineering, vol. 117, pp. 121–130, Mar. 2018, doi: 10.1016/j.cie.2018.01.023. [22] H. Xiong and X. Diao, “A review of cable-driven rehabilitation devices,” Disability and Rehabilitation: Assistive Technology, vol. 15, no. 8, pp. 885–897, Nov. 2020, doi: 10.1080/17483107.2019.1629110. [23] Y. Mao and S. K. Agrawal, “Design of a Cable-Driven Arm Exoskeleton (CAREX) for Neural Rehabilitation,” IEEE Transactions on Robotics, vol. 28, no. 4, pp. 922–931, Aug. 2012, doi: 10.1109/TRO.2012.2189496. [24] C. J. De Luca, M. Kuznetsov, L. D. Gilmore, and S. H. Roy, “Inter-electrode spacing of surface EMG sensors: Reduction of crosstalk contamination during voluntary contractions,” Journal of Biomechanics, vol. 45, no. 3, pp. 555–561, Feb. 2012, doi: 10.1016/j.jbiomech.2011.11.010. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93279 | - |
| dc.description.abstract | 頭頸部的活動度會影響與他人及環境互動的能力，也因此導致控制頸部運動上有困難的族群，如低頭綜合症患者，他們的獨立生活能力降低。傳統輔具多採用固定式裝置確保頸部的安全，但此靜態拘束仍舊限制了互動能力並常因為長時間穿戴下肌肉緊繃產生不舒適感，因此有必要開發可協助頸部肌肉控制動態活動且穿戴舒適的頸部輔具。為確保所採用機構不影響頸部的活動範圍，本研究利用並聯式線驅動機構設計軟性頸部穿戴裝置，使用渦卷彈簧作為集線器並提供線張力以輔助肌肉維持不同頸部姿勢與運動，為量化輔助程度，在頸部運動實驗中以肌電訊號評估肌肉激發強度的變化，結果顯示在抬頭時，頭夾肌在穿戴裝置下的激發強度比未穿戴小19.7%，可得裝置往後的線拉力確實減低支撐頭重的負擔。為量測頸部旋轉角度以提供醫師診斷與治療的參考，集線器加入角度感測的磁編碼器以藉由集線器旋轉圈數來計算機構中每條線長的變化，在使用頸部運動的量測結果進行機器學習後獲得運動學模型，使用未加入模型訓練的數據進行驗證得到方均根誤差值為1.68°。總體而言，本研究所開發的裝置可提供被動輔助與量測頭頸部旋轉角度，未來可朝提供主動式線張力控制發展以提供更有效的頸部運動輔助。 | zh_TW |
| dc.description.abstract | The movement of the head-neck plays a crucial role in interacting with others and the environment. Thus, individuals with neck movement control difficulties, such as patients with dropped head syndrome, often have lower independent living ability. Traditional assistive devices are mostly static collars to ensure neck safety. However, such static constraints limit interaction capabilities and often lead to discomfort due to prolonged muscle tension. Hence, there is a need to develop neck braces that assist in dynamic movement control while being comfortable to wear. To ensure that the chosen mechanism does not hinder the range of neck motion, this study designs a soft neck brace using a cable-driven parallel mechanism. A spiral spring is employed as a spool to provide cable tension, assisting muscle activation. Electromyography was used to assess muscle activation levels during neck movement experiments. The results indicated that during head extension, the activation level of the splenius capitis muscle was reduced by 19.7% when wearing the device, demonstrating that the posterior cable tension of the device effectively supports part of the head's weight. For measuring neck rotation angles to aid in diagnosis and treatment, the hub was integrated with a magnetic encoder for spool rotation angle measurement to obtain cable length. A machine learning-based model was derived based on cable lengths and neck movement measurements. Validation yielded a root mean square error (RMSE) of 1.68°. Overall, the device proposed in this study can provide passive assistance and measure head-neck rotation angles. Future works could focus on active neck movement assistance. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-07-23T16:39:09Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-07-23T16:39:09Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii ABSTRACT iii 目 次 iv 圖 次 vi 表 次 ix 縮寫對照表 x 符號彙編 xi 第1章 緒論 1 1.1 研究動機與背景 1 1.2 文獻回顧 1 1.2.1 低頭綜合症 1 1.2.2 頭頸部輔具 2 1.3 研究目的 9 第2章 裝置與系統設計 10 2.1 裝置設計 10 2.1.1 肩部背帶 11 2.1.2 頭部彈性綁帶 13 2.1.3 Arduino與感測裝置 14 2.2 運動學分析 15 2.3 性能測試 18 2.3.1 磁編碼器測試 18 2.3.2 整體裝置性能量測 19 第3章 實驗設計與分析 23 3.1 實驗設備介紹 23 3.1.1 光學動作捕捉系統 23 3.1.2 表面肌電量測系統（EMG） 24 3.2 人體實驗準備與流程 24 3.2.1 實驗設備與準備 26 3.2.2 流程與記錄 27 3.3 數據分析方法 28 3.3.1 頭部運動角度轉換 28 3.3.2 肌電訊號濾波 32 3.3.3 數據分割與合併 33 3.4 實驗結果與討論 35 3.4.1 頭部活動角度分析結果 36 3.4.2 頭部動作與頸部肌群激發程度之關係 39 3.4.3 頸部肌群激發程度比較結果 42 第4章 機器學習 45 4.1 機器學習簡介與文獻回顧 45 4.2 機器學習介紹 46 4.2.1 資料前處理與分割 46 4.2.2 模型訓練與演算法 47 4.2.3 神經網路法 48 4.2.4 模型驗證 49 4.2.5 特徵工程 50 4.3 運算設備與模型介紹 51 4.3.1 多層神經網路架構（MLP） 51 4.3.2 長短期記憶模型（LSTM） 51 4.4 人體實驗與數據 54 4.5 機器學習預測結果與討論 54 4.5.1 MLP與LSTM模型學習成效 54 4.5.2 LSTM模型預測結果 56 第5章 結論 62 參考文獻 63 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 頸部輔具 | zh_TW |
| dc.subject | 線驅動機構 | zh_TW |
| dc.subject | 頭頸動作分析 | zh_TW |
| dc.subject | 肌電訊號 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | Neck assistive device | en |
| dc.subject | Cable-driven | en |
| dc.subject | parallel mechanism | en |
| dc.subject | Machine learning | en |
| dc.subject | Electromyograms | en |
| dc.subject | Head and neck motion analysis | en |
| dc.title | 整合機器學習於線纜式頸部角度量測裝置 | zh_TW |
| dc.title | Integrating Machine Learning to Cable-Based Neck Angle Measurement Device | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 李志中;陳羽薰 | zh_TW |
| dc.contributor.oralexamcommittee | Jyh-Jone Lee;Yu-Hsun Chen | en |
| dc.subject.keyword | 頸部輔具,線驅動機構,機器學習,肌電訊號,頭頸動作分析, | zh_TW |
| dc.subject.keyword | Neck assistive device,Cable-driven,parallel mechanism,Machine learning,Electromyograms,Head and neck motion analysis, | en |
| dc.relation.page | 64 | - |
| dc.identifier.doi | 10.6342/NTU202401585 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-07-22 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 機械工程學系 | - |
| dc.date.embargo-lift | 2029-07-19 | - |
| 顯示於系所單位： | 機械工程學系 | |
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