Title

基於高齡化的家庭醫療保健技術

Translated Titles

Home Healthcare Technologies for Active Ageing

Authors

趙彥揚

Key Words

心電圖 ; 跌倒偵測 ; 模糊理論 ; ECG ; Fall detection ; Fuzzy Theorem ; MSP430 ; Arduino ; BLE

PublicationName

清華大學電機工程學系所學位論文

Volume or Term/Year and Month of Publication

2013年

Academic Degree Category

碩士

Advisor

馬席彬

Content Language

英文

Chinese Abstract

近年來醫療保健系統是一個新興的話題。這個系統可以為患者節省時間,改善患者的生活,並且減少濫用的醫療成本。這項研究目標是開發一個攜帶型心電圖即時監測系統並且結合具有能區分日常生活動作和跌倒的偵測裝置。在本篇論文分為兩個部分,一個是心電圖即時監測,另一個是應用模糊理論的跌倒偵測系統。該系統包含一個無線感測節點和一個中繼站,無線感測節點用來擷取身體的生物信號,中繼站的功能則是把截取後的生理訊號即時顯示在螢幕上以及利用3G或WiFi網路傳送到遠端的伺服器。無線感測節點包含一個類比前端放大器、微處理器、類比數位轉換器以及低耗電藍芽4.0版本。我們也利用Android平台作為一個資料中繼站,將數據傳輸到遠端的伺服器。在跌倒偵測的部分,我們使用三軸加速器來建立跌倒時的特徵。為了提高容錯和準確率,我們提出一個行為估計方法,其中包含加速度的變化和模糊理論來估算實驗對象的行為。最後我們使用MATLAB內建的模糊理論工具箱來進行模擬及驗證。結果顯示我們可以從160次的日常生活動作姿態中區分跌倒,敏感性為95%和特異性為97.5%。

English Abstract

Healthcare system is an emerging topic in recent years. It can save time for patients, improve patients life, and reduce the abuse cost for medical treatment. The goal of this research is to develop a real-time portable Electrocardiography (ECG) monitoring device, and fall detection that is capable of discriminating between Activities of Daily Life (ADL) and falls. There are two main parts which discussed in this thesis. One is ECG real-time monitoring. Another fall detection is using fuzzy logic. The system contains wireless sensor node that capturing the bio-signal of the body and a mobile hub that wireless sensor node can send information to the mobile hub. The wireless sensor node consists an analog front-end amplifier, an MCU that control the inside analog digital converter (ADC), and a bluetooth module with 4.0 Bluetooth low energy (BLE) version. We even use Android platform as a hub to transfer data to remote server. In fall detection part, we use 3-axis accelerometer to develop an effective fall detection algorithm based on the characteristics of falls. To increase the error tolerance and increase accuracy rate , we propose a behavior estimation method which consists of the change of acceleration (COA) and fuzzy rule based system to estimate the subject’s behavior. Then, we use MATLAB Fuzzy Logic Toolbox to simulate and estimate the behavior. Results show that falls can be distinguished from ADL with a sensitivity over 95% and a specificity of 97.5%, for a total set of 160 movements.

Topic Category 電機資訊學院 > 電機工程學系所
工程學 > 電機工程
Reference
  1. [7] R. Fensli, E. Gunnarson, and O. Hejlesen, “A wireless ECG system for continuous event recording and communication to a clinical alarm station,” in IEEE International Confer- ence of the Engineering in Medicine and Biology Society (IEMBS), vol. 1, San Francisco, CA, Sept. 2004, pp. 2208–2211.
    連結:
  2. [8] Y. Y. ChenWu, H. P. Ma, C. Biswas, and D. Markovic, “Universal architecture prototype for patient-centric medical environment,” in IEEE International Symposium on VLSI Design, Automation, and Test (VLSI-DAT), Hsinchu, Taiwan, April 2012, pp. 1–4.
    連結:
  3. [12] K.Ho, K.Yamamoto, N.Tsuchiya, H.Nakajima, K.Kuramoto, S.Kobashi, and Y.Hata, “Multi sensor approach to detection of heartbeat and respiratory rate aided by fuzzy logic,” in IEEE International Conference on Fuzzy Systems (FUZZ), Barcelona, Spain, July 2010, pp. 1–6.
    連結:
  4. [15] F. Sposaro and G. Tyson, “iFall: An android application for fall monitoring and re- sponse,” in IEEE International Conference Engineering in Medicine and Biology Society (EMBC), Minnesota, USA, Sept. 2009, pp. 6119–6122.
    連結:
  5. [17] A. Bourke, K. J. O’Donovan, J. Nelson, and G. M. OLaighin, “Fall-detection through vertical velocity thresholding using a tri-axial accelerometer characterized using an optical motion-capture system,” in IEEE International Conference on Engineering in Medicine and Biology Society (EMBS), Vancouver, Canada, Aug. 2008, pp. 2832–2835.
    連結:
  6. [18] J. Y. Hwang, J. Kang, Y. Jang, and H. Kim, “Development of novel algorithm and real- time monitoring ambulatory system using bluetooth module for fall detection in the el- derly,” in IEEE International Conference on Engineering in Medicine and Biology Soci- ety (IEMBS), vol. 1, California, USA, Sept. 2004, pp. 2204–2207.
    連結:
  7. [21] M. Kangas, A. Konttila, I. Winblad, and T. Jamsa, “Determination of simple thresholds for accelerometry-based parameters for fall detection,” in IEEE International Con- ference on Engineering in Medicine and Biology Society (EMBS), Lyon, France, Aug. 2007, pp. 1367–1370.
    連結:
  8. [22] A. Bourke, P. W. J. Van de Ven, A. Chaya, G. OLaighin, and J. Nelson, “Design and test of a longterm fall detection system incorporated into a custom vest for the elderly,” in IEEE International Conference on Signals and Systems (ISSC), Galway, Ireland, June 2008, pp. 307–312.
    連結:
  9. [23] M. Kangas, A. Konttila, P. Lindgren, I. Winblad, and T. Jamsa, “Comparison of low complexity fall detection algorithms for body attached accelerometers,” Journal on Gait & Posture, vol. 28, no. 2, pp. 285–291, Aug. 2008.
    連結:
  10. [25] S. Luo and Q. Hu, “A dynamic motion pattern analysis approach to fall detection,” in IEEE International Workshop on Biomedical Circuits and Systems (BioCAS), 2004, pp. 1–5–8a.
    連結:
  11. [28] L.Zadeh and J. Kacprzyk, Fuzzy Logic for the Management of Uncertainty,1sted. New York, NY, USA: Wiley professional computing, July 1992.
    連結:
  12. [30] D. Anderson, R. Luke, J. Keller, M. Skubic, M. Rantz, and M. Aud, “Modeling human activity from voxel person using fuzzy logic,” IEEE Transactions on Fuzzy Systems (FUZZ), vol. 17, no. 1, pp. 39–49, Feb. 2009.
    連結:
  13. [33] D.Chen, W.Feng, Y.Zhang, X.Li, and T.Wang,“A wearable wireless fall detection system with accelerators,” in IEEE International Conference on Robotics and Biomimetics (ROBIO), Phuket, Thailand, Dec. 2011, pp. 2259–2263.
    連結:
  14. [1] W. T. Tang, C. M. Hu, and C. Y. Hsu, “A mobile phone based homecare management system on the cloud,” in IEEE International Conference on Biomedical Engineering and Informatics (BMEI), vol. 6, Shandong, China, Oct. 2010, pp. 2442–2445.
  15. [2] J. E. Hall, Guyton and Hall Textbook of Medical Physiology, 12th ed. Philadelphia, PA, USA: Saunders, June 2010.
  16. [3] Texas Instruments. (2013, July) MSP-EXP430F5438 Experimenter Board User’s Guide. [Online]. Available: http://www.ti.com/lit/ug/slau263h/slau263h.pdf
  17. [4] Google Inc. (2013) Android Developers. [Online]. Available: http://developer.android. com/index.html
  18. [5] R. W. S. Alan V. Oppenheim, Discrete-Time Signal Processing, 3rd ed. New York, NY, USA: Prentice Hall, Aug. 2009.
  19. [6] D. Simunic, S. Tomac, and I. Vrdoljak, “Wireless ECG monitoring system,” in IEEE International Conference on Wireless Communication, Vehicular Technology, Informa- tion Theory and Aerospace Electronic Systems Technology (VITAE), Aalborg, Denmark, May 2009, pp. 73–76.
  20. [9] M. Shoaib, R. Dragon, and J. Ostermann, “View-invariant fall detection for elderly in real home environment,” in IEEE International Conference on Pacific-Rim Symposium in Image and Video Technology (PSIVT), Singapore, Nov. 2010, pp. 52–57.
  21. [10] X. Yu, “Approaches and principles of fall detection for elderly and patient,” in IEEE International Conference on E-health Networking, Applications and Services (Health- Com), Singapore, July 2008, pp. 42–47.
  22. [11] K. Appiah, A. Hunter, and C. Waltham, “Low-power and efficient ambient assistive care system for elders,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Colorado, USA, June 2011, pp. 97–102.
  23. [13] Z. Zhao, Y. Chen, J. Liu, and Z. Zhao, “Fall detecting and alarming based on mobile phone,” in IEEE International Conference on Ubiquitous Intelligence Computing and Autonomic Trusted Computing (UIC/ATC), Shaanxi, China, Oct. 2010, pp. 494–497.
  24. [14] V. Q. Viet, G. Lee, and D. Choi, “Fall detection based on movement and smart phone technology,” in IEEE International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), Ho Chi Minh City, Vietnam, March 2012, pp. 1–4.
  25. [16] Y. Yang and X. Zhao, “Development of a fall detection algorithm based on a tri-axial accelerometer,” in IEEE International Conference on Biomedical Engineering and Informatics (BMEI), vol. 3, Shanghai, China, Oct. 2011, pp. 1371–1374.
  26. [19] J. Zheng, G. Zhang, and T. Wu, “Design of automatic fall detector for elderly based on triaxial accelerometer,” in IEEE International Conference on Bioinformatics and Biomedical Engineering (ICBBE), Beijing, China, June 2009, pp. 1–4.
  27. [20] Q. Li, J. Stankovic, M. Hanson, A. Barth, J. Lach, and G. Zhou, “Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information,” in IEEE International Workshop on Wearable and Implantable Body Sensor Networks (BSN), California, USA, June 2009, pp. 138–143.
  28. [24] D.-U. Jeong, S.-J. Kim, and W.-Y. Chung, “Classification of posture and movement using a 3-axis accelerometer,” in IEEE International Conference on Convergence Infor- mation Technology (CIT), Gyeongju, South Korea, Nov. 2007, pp. 837–844.
  29. [26] Y.Hata, K.Kuramoto, S.Kobashi, and H.Nakajima,“A survey of fuzzy logic in medical and health technology,” in IEEE International Conference World Automation Congress (WAC), Puerto Vallarta, Mexico, June 2012, pp. 1–6.
  30. [27] T. Tanaka, T. Fujita, K. Sonoda, M. Nii, K. Kanda, K. Maenaka, A. C. C. Kit, S. Okochi, and K. Higuchi, “Wearable health monitoring system by using fuzzy logic heart-rate extraction,” in IEEE International Conference World Automation Congress (WAC), Puerto Vallarta, Mexico, June 2012, pp. 1–4.
  31. [29] T. Gatton and M. Lee, “Fuzzy logic decision making for an intelligent home healthcare system,” in IEEE International Conference on Future Information Technology (FutureTech), Busan, South Korea, May 2010, pp. 1–5.
  32. [31] ANALOG DEVICES. (2010, Jan.) ADXL335: SMALL, LOW POWER, 3- AXIS ±3G ACCELEROMETER. [Online]. Available: http://www.analog.com/static/ imported-files/data sheets/ADXL335.pdf
  33. [32] X. H. Wu, M. C. Su, and P. C. Wang, “A hand-gesture-based control interface for a car-robot,” in IEEE International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan, Oct. 2010.
  34. [34] A. Bourke, P. van de Ven, M.Gamble, R. O’Connor, K. Murphy, E. Bogan, E. McQuade, P. Finucane, G. OLaighin, and J. Nelson, “Assessment of waist-worn tri-axial accelerometer based fall detection algorithms using continuous unsupervised activities,” in IEEE International Conference on Engineering in Medicine and Biology Society (EMBC), Buenos Aires, Argentina, Sept. 2010, pp. 2782–2785.
  35. [35] F. Bianchi, S. Redmond, M. Narayanan, S. Cerutti, and N. Lovell, “Barometric pressure and triaxial accelerometry-based falls event detection,” IEEE Transactions on Neural Systems and Rehabilitation Engineering (NSRE), vol. 18, no. 6, pp. 619–627, Dec. 2010.