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作者(中文):陳君瑞
作者(外文):Chen, Chun-Jui.
論文名稱(中文):利用慣性量測單元的四肢復健系統
論文名稱(外文):IMU-based Rehabilitation System for Upper and Lower Limbs
指導教授(中文):王俊堯
指導教授(外文):Wang, Chun-Yao
口試委員(中文):李思慧
李昀儒
口試委員(外文):Lee, Si-Huei
Lee, Yun-Ju
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學號:106062603
出版年(民國):108
畢業學年度:107
語文別:英文
論文頁數:22
中文關鍵詞:復健系統
外文關鍵詞:RehabilitationIMU
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本研究著重於復健動作的判定,由於患者在醫院時能藉由醫生的指示完成標準的復健動作,但復健是需要患者平時在家有空就作,但患者在家並無法準確地了解其復健動作是否標準,因此我們設計一個復健系統結合慣行測量單元與穿戴式裝置來偵測患者復健姿勢的正確性,並連接手機APP,即時地告知患者是否有正確的達成復健動作,復健完成後,系統將儲存復健資料供患者及醫生參考,一方面患者可以了解其復原狀況,另一方面醫生可藉由此資料給予更好的治療,進而加快患者的患肢康復,目前針對五十肩、膝關節及髖關節復健運動進行研究。
In this work, we present an IMU-based rehabilitation system for upper and lower limbs. This system uses two wearable IMU sensors to detect rehabilitation motions of patients suffering from frozen shoulder, knee surgery, and hip surgery. The sensors are also connected to a smartphone via Bluetooth, and an Android APP is designed to show the correctness and the statistics of the rehabilitation exercises. The experimental results show that the average errors of knee angle, and elbow angle are both less than 5 degrees. The average recognition rates of all the rehabilitation exercises are larger than 85%.
摘要
目錄
第一章-------------1
第二章-------------4
第三章-------------13
第四章-------------17
第五章-------------20
Bibliography-------21
1. PAPAMAMA rehabilitation system, https://www.longgood.com.tw/papamama

2. Rehabilitation system at home, http://khpo.iiiedu.org.tw

3. Gianni Fenu, and Gary Steri, "IMU based post-traumatic rehabilitation assessment", International Symposium on Applied Sciences in Biomedical and Communication Technologies, 2011.

4. W. Kong, S. Sessa, S. Cosentino, M. Zecca, K. Saito, C. Wang, U. Imtiaz, Z. Lin, L. Bartolomeo, H. Ishii, T. Ikai, A. Takanishi, "Development of a real-time IMU-based motion capture system for gait rehabilitation", IEEE International Conference on Robotics and Biomimetics, 2013.

5. Norhafizan Ahmad, Raja Ariffin Raja Ghazilla, and Nazirah M. Khairi, "Reviews on Various Inertial Measurement Unit (IMU) Sensor Applications", International Journal of Signal Processing Systems vol. 1, no. 2, 2013.

6. S. I. Lee, C. P. Adans-Dester, M. Grimaldi, A. V. Dowling, P. C. Horak, R. M. Black-Schaffer, P. Bonato, and J. T. Gwin, "Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training", IEEE Journal of Translational Engineering in Health and Medicine, vol. 6, 2018.

7. H. Mizuno, H. Nagai, K. Sasaki, H. Hosaka, C. Sugimoto, K. Khalil, and S. Tatsuta, "Wearable Sensor System for Human Behavior Recognition (First Report: Basic Architecture and Behavior Prediction Method)", Proc. of International Conference on Solid-State Sensors, Actuators, and Microsystems, pp. 435-438, 2007.

8. T. C. Wang, and C. Y. Wang, "A Smart Knee Pad for Stride Count and Walking Distance Measurement via Knee Angle Calculation", Master Thesis, National Tsing Hua University, Hsinchu, Taiwan, R.O.C., 2018.

9. Y. P. Chang, and C. Y. Wang,"A Smart Single-Sensor Device for Instantaneously Monitoring Lower Limb Exercises", Master Thesis, National Tsing Hua University, Hsinchu, Taiwan, R.O.C., 2018.

10. Gyro gesture control, http://www.gyro.com.tw/index.php/zh-tw/products-zh-tw/lot-smartsensor-zh-tw
 
 
 
 
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