(3.80.6.131) 您好!臺灣時間:2021/05/17 03:10
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:林于玄
研究生(外文):Yu-Hsuan Lin
論文名稱:支援物聯網協定以肌電感測為基礎之穿戴式運動識別系統
論文名稱(外文):EMG-based Wearable Exercise Recognition System Supporting Internet of Things Protocols.
指導教授:李昭賢李昭賢引用關係
指導教授(外文):Chao-Hsien Lee
口試委員:夏志賢陳彥霖蕭榮修
口試委員(外文):Chao-Hsien LeeChao-Hsien LeeChao-Hsien Lee
口試日期:2016-07-19
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電子工程系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:104
語文別:中文
中文關鍵詞:物聯網穿戴式運動識別肌電訊號
外文關鍵詞:Internet of Things (IoT)WearableExercise RecognitionElectromyography (EMG)
相關次數:
  • 被引用被引用:0
  • 點閱點閱:196
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
世界衛生組織(WTO)指出運動量不足已成全球第四大致死因素,缺乏運動易造成罹患心血管疾病、糖尿病、肥胖…等慢性疾病的風險。此外,也會提高心情憂鬱及焦慮的機率。因此,從過去研究顯示增進身心健康的最佳方法就是養成並維持運動,由於身體活動需達到足夠運動強度才能真正保健之目的,故本研究動機為可識別出不同運動強度之運動,包含:輕度運動強度的(i) 走路、中度運動強度的(ii) 快走、(iii) 慢跑及(iv)高度運動強度的跑步等四種強度不同的運動型態。不同於以往之穿戴式運動感測裝置大多使用陀螺儀(Gyroscope)及加速度計(Accelerometer),本論文使用肌電訊號(EMG)感測器測量使用者運動過程肌電訊號(EMG)的變化,開發一套能監控外出運動狀態之穿戴式識別系統,其中:Message Queuing Telemetry Transport (MQTT)協定結合BLE將數據傳遞到使用者隨身裝置進行運算以識別出運動模式,最後本研究設計(i)單一運動和(ii)混合運動模式實驗測試結果,其正確率(含步數計算)可達88%以上,基於上述驗證,顯示本實驗設計之穿戴式系統可達到計算步數,同時兼顧其正確率。
World Health Organization (WTO) noted that lack of exercise has become the worlds fourth major cause of death. That is, lack of exercise easily causes cardiovascular disease, diabetes, obesity and other chronic diseases. It also causes feelings of depression and anxiety. Thus, doing exercise is the best method of keeping healthy body and mentality based on past research. However, physical activity needs to achieve sufficient exercise intensity and then be able to keep healthy. This paper is proposed to identify four types of exercises, i.e., rambling, walking, jogging and running. In this paper, different from using G-sensors, i.e., accelerometer and gyroscope, we utilize Electromyography (EMG) sensor to detect the exercise pattern and design one wearable exercise recognition system, in which EMG signals are transmitted using MQTT and Bluetooth to the remote portable device and then be processed and identified into exercise patterns. According to our experimental results, the proposed system has 88% accuracy. Hence, the proposed wearable system is proved count steps and high-accuracy.
中文摘要 i
ABSTRACT ii
誌 謝 iv
目 錄 v
表目錄 vii
圖目錄 viii
第一章緒論 1
1.1研究背景 1
1.2研究動機 2
1.3研究目的與具體目標 2
1.4論文架構 3
第二章 文獻探討 4
2.1運動強度與識別 4
2.2 肌電感測器相關應用 9
2.3物聯網協定 15
第三章 運動識別系統設計與架構 20
3.1運動識別系統架構 20
3.2裝置組成 21
3.3感測原理 25
3.4傳輸原理 30
3.5正規化系統流程 31
3.6運動識別系統流程 37
第四章 實驗結果與分析 41
4.1實驗環境 41
4.2實驗一 演算法辨識正確率 42
4.3實驗二 演算法系統辨識效率 44
4.4實驗三 動態運動辨識率測試 45
4.4.1實驗量測對象 46
4.4.2 單一運動模式 46
4.4.3混合運動模式 49
4.5實驗四 與其它APP比較 54
4.5.1步數正確率比較 56
4.5.2耗電量比較 58
第五章 結論與未來研究方向 64
參考文獻 66
附錄 70
附錄 71
附錄 72
[1]World Health Organization, “Physical Activity”, Who, Geneva, 2013.
[2]黃麗卿及黃國晉,“代謝症候群的定義與流行病學”, 臺灣醫學, 11. 4, 2007,第363-369頁。
[3]劉秉一, “中年人的危機-代謝症候群”, 重症醫學雜誌, 9. 1, 2008, 第26-33頁。
[4]林愉樺及陳坤檸, “老年人功能性體適能與身體活動之探討”, 臺中科大體育學刊, 10,2014, 第89-96頁。
[5]Lippincott Williams, and Wilkins, “ACSMs health-related physical fitness assessment manual”, American College of Sports Medicine, vol. 5, pp. 1-173, 2013.
[6]林晉榮及黃珍鈺, “青少年憂鬱情緒與休閒運動效益之探討”, 中華體育季刊, 4. 19, 2005, 第26-31頁。
[7]呂惠富, “休閒運動之參與動機與休閒效益關係之研究”, 休閒產業管理學刊, 1. 1, 2008, 第41-53頁。
[8]H. Vivian, and A. Gibson, “Advanced fitness assessment, and exercise prescription 7th edition”, Human kinetics, 2014, pp. 1-552.
[9]吳宜宸,“銀髮族的健康促進生活型態與休閒時間身體活動的關係:社會支持的角色”, 碩士論文, 國立高雄餐旅大學旅遊管理系, 2010。
[10]L. Williams, and M. Wilkins, “ ACSMs guidelines for exercise testing and prescription”, American College of Sports, vol. 8, pp. 1-455, 2013.
[11]A. Jalal, Y. Kim, S. Kamal, A. Farooq, and D. Kim, “Human daily activity recognition with joints plus body features representation using Kinect sensor”, Proceeding of International Conference on Informatics Electronics & Vision (ICIEV), Fukuoka, Japan, Jun. 15-18, 2015, pp. 1-6.
[12]H. Koskimäki, and P. Siirtola, “Recognizing gym exercises using acceleration data from wearable sensors”, Proceeding of IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Orlando, USA , Dec. 9-12, 2014 , pp. 321-328.
[13]M. W. Lee, A. M. Khan, and T. S. Kim, “A single tri-axial accelerometer-based real-time personal life log system capable of human activity recognition and exercise information generation”, Personal and Ubiquitous Computing, vol. 1, no. 779, pp. 887-898, 2011.
[14]B. J. Mortazavi, M. Pourhomayoun, G. Alsheikh, N. Alshurafa, S. I. Lee, and M. Sarrafzadeh, “Determining the single best axis for exercise repetition recognition and counting on smartwatches”, Proceeding of IEEE 11th International Conference on Wearable and Implantable Body Sensor Networks, Zurich, Switzerland, Jun. 16-19, 2014, pp. 33-38.
[15]G. Bajrami, M. O. Derawi, and P. Bours, “Towards an automatic gait recognition system using activity recognition (wearable based)”, Proceeding of IEEE third International Workshop Security and Communication Networks (IWSCN), Gjovik, Norway, May. 18-20, 2011, pp. 23-30.
[16]R. C. Wagenaar, I. Sapir, Y. Zhang, S. Markovic, L. M. Vaina, and T. D. Little. “Continuous monitoring of functional activities using wearable, wireless gyroscope and accelerometer technology”, Proceeding of IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, USA, Aug. 30 -Sept. 3, 2011, pp. 4844-4847.
[17]Y. Ibata, S Kitamura, K. Motoi, and K. Sagawa, “Measurement of three-dimensional posture and trajectory of lower body during standing long jumping utilizing body-mounted sensors”, Proceeding of 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, July. 3-7, 2013, pp. 4891-4894.
[18] 蘇怡賓,“以彎曲感測為基礎之穿戴式節能運動識別系統 ”, 碩士論文,國立臺北科技大學電子工程系, 2015。
[19]A. C. Guyton, “蓋統生理學─生理及疾病機轉”, 華杏,1.1, 1900.
[20]L. D. Weiss, J. M. Weiss and J. K. Silver, “Easy EMG: a guide to performing nerve conduction studies and electromyography”, Elsevier Health Sciences, 2015, pp. 1-304.
[21]江勁政, 肌電圖在網球運動之應用, 中華體育季刊, 1.30, 2016, 第49-56頁。
[22]B. I. Prilutsky, and R. J. Gregor, “ Swing-and support-related muscle actions differentially trigger human walk–run and run–walk transitions”, Journal of Experimental Biology, vol. 204 no. 13, pp. 2277-2287, 2001.
[23]I. Campanini, A. Merlo, P. Degola, R. Merletti, G. Vezzosi, and D. Farina, “Effect of electrode location on EMG signal envelope in leg muscles during gait”, Journal of Electromyography and Kinesiology, vol. 17, no. 4, pp. 515-526, 2007.
[24]F. Carrino, A. Ridi, M. Caon, O. A. Khaled, and E. Mugellini, “Optimization of an electromyography-based activity recognition system”, Proceeding of ISSNIP on Biosignals and Biorobotics Conference (BRC), Rio de Janerio, Brazil, Feb. 18-20, 2013, pp. 1-6.
[25]H. Pham, M. Kawanishi and T. Narikiyo, “A LLE-HMM-based framework for recognizing human gait movement from EMG”, Proceeding of IEEE International Conference on Robotics and Automation (ICRA), Seattle, USA, May. 26-30, 2015, pp. 2997-3002.
[26] Q. Wang, X. Chen, R. Chen, Y. Chen, and X. Zhang, “Electromyography-Based Locomotion Pattern Recognition and Personal Positioning Toward Improved Context-Awareness Applications”, Proceeding of IEEE Transactions on Systems, Man, and Cybernetics: Systems, Sept. 15, 2013, pp. 1216-1227.
[27] M. Jung, J. H. Kim, H.W. Wi, S. Kim, and M. Kovatsch, “Things-to-cloud communication: technology overview and design considerations”, Proceeding of IEEE 5th International Conference on the Internet of Things (IoT), Seoul, Korea, Oct. 26-28, 2015, pp. 1-2.
[28]V. Lampkin, W. T. Leong, L. Olivera, S. Rawat, N. Subrahmanyam, R. Xiang, G. Kallas, N. Krishna, S. Fassmann, M. Keen, and D. Locke, “Building smarter planet solutions with mqtt and ibm websphere mq telemetry”, IBM Redbooks, vol. 1, pp. 1-268, 2012.
[29]H. W. Chen, and F. J. Lin, “Converging MQTT resources in ETSI standards based M2M platform”, Proceeding of IEEE International Conference on Internet of Things (iThings) and IEEE Cyber, Physical and Social Computing (CPSCom) on Green Computing and Communications (GreenCom), Taipei, Taiwan, Sept. 1-3, 2014, pp. 292-295.
[30]Z. Shelby and C. Bormann, “6LoWPAN: The wireless embedded Internet”, John Wiley & Sons, Vol. 43, pp. 1-244, 2011.
[31]Bluetooth, S. I. G. “Bluetooth specification version 4.2”, Bluetooth SIG, 2014.
[32] C. Zhou, and X. Zhang, “Toward the Internet of Things application and management: A practical approach”, Proceeding of IEEE 15th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Sydney, Australia, June. 19, 2014. pp. 1-6.
[33]B. Xiao, M. Z. Asghar, T. Jämsä, and P. Pulii, “ " Canderoid": A mobile system to remotely monitor travelling status of the elderly with dementia”, Proceeding of International Joint Conference on Awareness Science and Technology and Ubi-Media Computing (iCAST-UMEDIA), Aizuwakamatsu, Japan, Nov. 2-4, 2013, pp. 648-654.
[34]S. J. Nam, J. Kang, and D. Moon, “Adaptation of 6LoWPAN to Ship Area Sensor Networks with wired Ethernet backbone and performance analysis”, Proceeding of IEEE 14th International Conference on Control, Automation and Systems (ICCAS), Seoul, Korea, Oct. 22-25, 2014, pp. 967-970.
電子全文 電子全文(網際網路公開日期:20210811)
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top