博碩士論文 106827002 詳細資訊




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姓名 楊宛臻(Wan-Jhen Yang)  查詢紙本館藏   畢業系所 生醫科學與工程學系
論文名稱 運用加速度計實現具多項生理功能量測之即時監控IOT平台
(An IOT platform for real-time physiological parameters monitoring by using multitasking accelerometer system)
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摘要(中) 本研究主要運用三軸加速度計(Accelerometer)結合單晶片及快閃記憶體來達到即時三維資料的多元運算。三軸加速度計為利用壓電晶片感測運動學中之加速度或角加速度參數,以輸出之電壓值表示測得之數據,更可以透過「物聯網」並結合電腦或其他攜帶式裝置記錄所有生理信號並連線到雲端。
三軸加速度計又稱重力感測器(G-sensor),主要是提供加速度變化的資訊,在工程上的應用非常廣泛。也因為固態微機電系統(Micro Electro Mechanical Systems , MEMS)的發展與普及,許多原本大尺寸的零件,都隨著MEMS製程的演進而越來越精細,尺寸越來越小,並且製作成本也隨著技術與時間的進展,隨之不斷的降低,到足以大量生產零件,並且以可接受的價格提供給市場,提供商業化的元件,如前述提到的三軸加速度計就是其中一種結合MEMS技術達到體積輕量化。另外磁場感應器(Compass, Magnetic Field)及傾斜度感應器(Orientation)等等也是常見結合MEMS的產品,可應用的範圍也愈來越多。
藉前述所提到的MEMS技術,可將如米粒般大小的晶片整合於穿戴式裝置中,並且透過不同的演算法及配戴方法,藉由不同姿態所造成的不同加速值進行演算,可量測多項生理功能,如活動紀錄、跌倒偵測、計步器、久坐偵測、心跳與呼吸偵測及睡眠分析,但針對不同訊號的量測,所需要的計算方法及取樣頻率也會有所不同,如計步器而言,取樣頻率10Hz就足以運算並獲得相應的走路或跑步狀態下較精確的步數。一般心跳及呼吸的量測都是需要透過醫院的儀器設備來量測,而且這兩種信號頻率經常是有相互交疊的現象,所以本研究將突破性開發結合三軸加速度計的穿戴式裝置來量測生理訊號。但也因為目前市面上常見的三軸加速度計對於微弱的震動感應較不靈敏,所以本研究需要透過軟韌體進行信號處理的方式,將微弱的心律震動與呼吸信號擷取出來,作為後續生理特徵檢測的信號來源。
有許多研究指出,三軸加速度計量測使用者動作訊號,較無量測位置限制,亦不需電極緊貼皮膚,在實務上較為方便,設計彈性也較大。本研究目的在發展個人生活型態模式分析,將穿戴式裝置三軸加速度計輸出的使用者動作及生理訊號轉換成判別活動強度及生理功能,並進而分析個人生活型態模式。
摘要(英) This study used a three-axis accelerometer combined with single-chip and flash memory for multivariate real-time 3D data. The three-axis accelerometer uses the piezoelectric principle to sense the acceleration or angular acceleration in kinematics and express the measured value in terms of output voltage also widely used in engineering applications. In the future, all physiological signals can be recorded and connected to the cloud through the Internet of Things (IOT)and combined with a computer or other portable device.
Because of the development and popularity of Micro Electro Mechanical Systems (MEMS), many of the original large-sized parts are getting smaller and smaller with the MEMS process. Production costs continue to decrease as technology and time evolve, enough to mass produce parts, and to the market at an acceptable price, providing commercial components. The aforementioned three-axis accelerometer is one of the requirements for combining MEMS technology to achieve small size and low price. In addition, the magnetic field sensor (Compass, Magnetic Field) and the tilt sensor (Orientation) are also common MEMS products, and the range of applications is increasing.
The aforementioned MEMS technology can integrate a rice-sized sensor into a wearable device, and through different algorithms and wearing methods, calculate different acceleration values caused by different postures, and measure a plurality of physiological signal. Such as activity records, fall detection, pedometer, sedentary detection, heartbeat and respiratory detection, and sleep analysis. However, the calculation method and sampling frequency required for different signal measurements will also be different. For example, a pedometer sampling frequency of 10 Hz is sufficient to calculate and obtain a corresponding number of steps in a walking or running state. Generally, the measurement of heart rate and breathing needs to be measured through hospital equipment, and the two signal frequencies often overlap each other. Therefore, this study will be a breakthrough development of a wearable device combined with a three-axis accelerometer to measure physiological signals. Because the three-axis accelerometers module currently on the market are less sensitive to weak vibration sensing, this study needs to use signal processing by software and firmware to extract weak heart rhythm vibrations and respiratory signals as signals for subsequent physiological feature detection.
There are many studies that indicate that the three-axis acceleration measurement user motion signal is less restrictive than the measurement position, and does not require the electrode to be close to the skin. It is more convenient in practice and has greater design flexibility. The purpose of this study is to record and analyze user motion and physiological signals through a three-axis accelerometer wearable device, which can be used to determine the intensity of activity and physiological state, and then to analyze the pattern of personal life.
關鍵字(中) ★ 微控制器
★ 藍牙
★ 加速度計
★ 穿戴式裝置
★ 數位信號處理
關鍵字(英) ★ Microcontroller
★ Bluetooth
★ accelerometer
★ Wearable Device
★ Digital Signal Processing
論文目次 中文摘要 I
英文摘要 II
誌謝 IV
目錄 V
圖目錄 IX
表目錄 XII
符號說明 XIII
一、 緒論 1
1-1 研究動機 1
1-2 文獻回顧 6
1-3 研究目的 10
二、 研究原理 12
2-1 三軸加速度計運作 12
2-2 活動紀錄 15
2-3 跌倒偵測 16
2-4 心震訊號 16
2-5 數位訊號處理 18
2-5-1 類比數位轉換器 19
2-5-2 解析度 19
2-5-3 取樣率 20
2-5-4 快速傅立葉變換 22
2-5-5 數位濾波器 24
2-5-6 加伯轉換 30
2-6 單晶片微電腦 31
2-7 三軸加速度感測模組 32
2-8 記憶體 33
2-9 積體電路匯流排通訊介面 34
2-9-1 硬體結構 34
2-9-2 通訊協定 35
2-10 序列周邊介面通訊 36
2-10-1 硬體結構 36
2-10-2 通訊協定 37
2-11 通用非同步收發傳輸器 39
2-11-1 硬體結構 39
2-11-2 通訊協定 39
2-12 電源供應 40
2-13 藍牙 41
三、 研究方法 42
3-1 硬體 42
3-3-1 單晶片 43
3-1-2 記憶體 43
3-1-3 加速度感測器 44
3-1-4 電源供應 45
3-2 韌體 46
3-2-1 撰寫軟體 46
3-2-2 通訊方式 46
3-2-3 類比數位位元轉換 47
3-3 藍牙 47
3-3-1 物理層 47
3-3-2 連結層 47
3-3-3 主機控制接口層 48
3-3-4 邏輯連接控制及自適應協議層 48
3-3-5 安全協議層 48
3-3-6 屬性協議層 48
3-3-7 通用屬性配置文件層 48
3-4 訊號處理 49
3-4-1 快速傅立葉轉換 49
3-4-2 時域與頻域分析 50
四、 實驗結果 51
4-1 硬體成果與包裝外殼 51
4-1-1 硬體電路圖 51
4-1-2 電路板製作 52
4-1-3 prototype 53
4-1-4 電路板成品圖 54
4-1-5 3D繪圖外殼 55
4-1-6 外殼實體 56
4-2 訊號處理 58
4-2-1 位元轉數字 58
4-2-2 原始訊號圖 59
4-2-3 快速傅立葉轉換 59
4-2-4 實質比對 61
五、 結果與討論 62
5-1 結果 62
5-2 討論 62
參考文獻 63
參考文獻 ﹝1﹞ 衛生福利部106年國人死因統計結果。2017年6月15日,取自https://www.mohw.gov.tw/cp-3795-41794-1.html。
﹝2﹞ 國家發展為委員會人力推估。2018年8月30日,取自https://pop-proj.ndc.gov.tw/download.aspx?uid=70&pid=70。
﹝3﹞ Mojtaba Jafari Tadi, Tero Koivisto, Mikko Pänkäälä, and Ari Paasio, “Accelerometer-Based Method for Extracting Respiratory and Cardiac Gating Information for Dual Gating during Nuclear Medicine Imaging”, Hindawi Publishing Corporation International Journal of Biomedical Imaging, Vol 2014, Article ID 690124, 11 pages, July 2014
﹝4﹞ Chenxi Yang , Student Member, IEEE, and Negar Tavassolian, “Combined Seismo- and Gyro-Cardiography: A More Comprehensive Evaluation of Heart-Induced Chest Vibrations”, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol 22, pp. 1466 - 1475, September 2018
﹝5﹞ Crow, R.S., Hannan P., Jacobs D., Hedquist L., Salerno D.M., “Relationship between Seismocardiogram andEchocardiogram for Events in the Cardiac Cycle” Am. J. Noninvasive Cardiol. 39–46, August 1994
﹝6﹞ Amirtaha Taebi, and Hansen A. Mansy, “Time-frequency Analysis of Vibrocardiographic Signals”, In Proceedings of the 2015BMES Annual Meeting, Tampa, FL, USA, October 2015.
﹝7﹞ Toshiyo Tamura, Takumi Yoshimura, Masaki Sekine, Mitsuo Uchida, and Osamu Tanaka, “A Wearable Airbag to Prevent Fall Injuries”, IEEE Transactions on Information Technology in Biomedicine, Vol 13, pp. 910 – 914, November 2009
﹝8﹞ Strong WB, Malina RM, Blimkie CJ, Daniels SR, Dishman RK , Gutin B, Hergenroeder AC, Must A, Nixon PA, Pivarnik JM, Rowland T, Trost S, Trudeau F., “Evidence based physical activity for school-age youth”, The Journal of Pediatrics, pp. 732–737, June 2005
﹝9﹞ Richard M. Pulsford, Mario Cortina-Borja, Carly Rich, Florence-Emilie Kinnafick, Carol Dezateux, Lucy J. Griffiths, “Actigraph Accelerometer-Defined Boundaries for Sedentary Behaviour and Physical Activity Intensities in 7 Year Old Children”, PLoS ONE, Volume 6, August 2011

﹝10﹞ D.F. Kripkeab, D.J. Mullaney, S. Messin, V.G. Wyborney, “Wrist actigraphic measures of sleep and rhythmsMesures actigraphiques du poignet au cours du sommeil et rythmes”, Electroencephalography and Clinical Neurophysiology, Vol 44, pp. 674-676, May 1978
﹝11﹞ Isaac Starr, A. J. Rawson, H. A. Schroeder, and N. R. Joseph, “Studies on the estimation of cardiac output in man, and of abnormalities in cardiac function, from the heart′s recoil and the blood′s impacts; The ballistocardiogram” The American Journal of Physiology, Vol 127, pp. 1-28, July 1939
﹝12﹞ Keya Pandia, Omer T Inan, Gregory T A Kovacs and Laurent Giovangrandi, “Extracting respiratory information from seismocardiogram signals acquired on the chest using a miniature accelerometer”, Physiological Measurement, Volume 33, pp. 1643–1660, September 2012
﹝13﹞ P. Bifulco, GD Gargiulo, G d’Angelo, A. Liccardo, M Romano, F Clemente, M Cesarelli, “Monitoring of respiration, seismocardiogram and heart sounds by a PVDF piezo film sensor”, Electric and Electronic Measurement for the Economic Upturn Benevento, pp. 786–789, September 2014
﹝14﹞ Omer T. Inan, Pierre-Francois Migeotte, Kwang-Suk Park, Mozziyar Etemadi, Kouhyar Tavakolian, Ramon Casanella, John Zanetti, Jens Tank, Irina Funtova, G. Kim Prisk and Marco Di Rienzo, “Ballistocardiography and Seismocardiography: A Review of Recent Advances”, IEEE Journal of Biomedical and Health Informatics, Vol 19, pp. 1414 - 1427, July 2015
﹝15﹞ Mojtaba Jafari Tadi, Tero Koivisto, Mikko Pänkäälä, Ari Paasio, Timo Knuutila, Mika Teräs, Pekka Hänninen, “A new algorithmfor segmentation of cardiac quiescent phases and cardiac time intervals using seismocardiography”, International Conference on Graphic and Image Processing, Vol 9443, 24–26, October 2015
﹝16﹞ AmirtahàTaebi, Brian E. Solar, Andrew J. Bomar, Richard H. Sandler and Hansen A. Mansy, “Recent Advances in Seismocardiography”, International Journal of Molecular Sciences, pp. 64–86, January 2019
﹝17﹞ 資料來源:維基百科,取自https://en.wikipedia.org/wiki/Digital_signal_processing
﹝18﹞ 資料來源:維基百科,取自https://en.wikipedia.org/wiki/Analog-to-digital_converter
指導教授 羅孟宗(Men-Tzung Lo) 審核日期 2019-7-16
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