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研究生： 林倪鋒
論文名稱： 以加速規為基礎之人體姿態穩定量測系統
An accelerometer based system for the measurement of postural stability
指導教授： 吳順德
學位類別： 碩士
Master
系所名稱： 機電工程學系
Department of Mechatronic Engineering
論文出版年： 2014
畢業學年度： 102
語文別： 中文
論文頁數： 65
中文關鍵詞： 身體晃動 、加速規 、零相位 、多變量多尺度熵指標
英文關鍵詞： postural sway, accelerometer, zero phase, multivariate multiscale entropy index
論文種類： 學術論文
相關次數： 點閱：451  下載：26 
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    • 本研究是以三軸加速規為基礎開發出低成本且高效能的「人體姿態穩定量測系統」，本系統包括個人電腦、微處理機與三個三軸加速規，三個加速規分別放置於受試者腰部，大腿和小腿的位置，微處理機能將類比加速度訊號轉換為數位訊號，接著將數位訊號依通訊協定編碼為數位資料並且透過RS232通訊介面傳回個人電腦，量測系統之取樣頻率達655Hz。
    比對以下三種不同的實驗方法：(1)觀看靜態影像時的身體晃動；(2)觀看暈眩動態影像時的身體晃動；(3)進行數學運算時的身體晃動。
    量測訊號的分析程序主要分為五個部份：(1)以一維小波去噪的方式濾除白雜訊；(2)使用零相位巴特沃斯低通濾波器濾除60Hz電力線干擾與50Hz以上的高頻干擾；(3)使用零相位巴特沃斯高通濾波器濾除2Hz以下的低頻干擾；(4)使用多變量多尺度熵分析濾波訊號(MMSE)；(5)將尺度1至尺度10計算出的MMSE加總得到多變量多尺度熵指標(MMEI)。
    本實驗總共有30位20至26歲的健康受試者參與，經過MMEIs計算後的結果顯示出在比對觀看靜態影像與進行數學運算時的實驗結果有顯著差異，且進行數學運算時的MMEIs計算結果小於觀看靜態影像結果，本研究顯示出本系統可評估身體潛在的平衡與姿態穩定能力。
    關鍵字：身體晃動、加速規、零相位、多變量多尺度熵指標

    In this study, we develop an inexpensive, efficient system for the measurement of postural stability using tri-axial accelerometers. This system consists of a personal computer, a microprocessor and 3 tri-axial accelerometers located on waist, thigh and ankle of a subject. The accelerometers were used to measure the accelerations caused by postural sway of the subjects. The microprocessor converts the analog acceleration signals into digital signals and then transmits the digital data to a personal computer via RS232 interface. The sampling rate of this measure system is up to 655Hz. Three different testing methods were compared: (1) measurement of postural sway during watching a still picture; (2) measurement of postural sway during watching a dizzy picture; (3) measurement of postural sway during doing mental calculations. The analysis procedures of the measured data consists of five major parts: (1) removing the sensor noise by using wavelet denoise method; (2) removing the power line inference (60Hz) by using a zero phase Butterworth low pass filter with the cutoff frequency at 50Hz; (3) removing the low frequency inference (60Hz) by using a zero phase Butterworth high pass filter with the cutoff frequency at 2Hz; (4) Analyzing the filtered signals by using the multivariate multiscale entropy (MMSE). (5) multivariate multiscale entropy index (MMEI) were obtained by summing the MMSEs from scale 1 to scale 10.
    There are thirty healthy subjects aged 20-26 years participate in this research project. The experimental results indicate that there exists a significant
    difference of MMEIs between watching-still-picture tests and doing-mental-calculations tests. The MMEIs obtained from doing-mental-calculations are smaller than that obtained from watching-still-picture tests. These studies show the potential of this system in the assessment of balance and postural stability.
    Keywords: postural sway, accelerometer, zero phase, multivariate multiscale entropy index.

    摘要..................................................... i 英文摘要................................................. ii 誌謝..................................................... iv 目錄..................................................... v 圖目錄.................................................. vii 表目錄................................................... ix 第一章緒論 .............................................. 1 1.1 前言 ................................................ 1 1.2 研究動機與目的....................................... 2 1.3 論文架構 ............................................ 3 第二章系統架構與相關研究................................. 4 2.1 平衡量測相關文獻..................................... 5 2.2 加速規感測器原理..................................... 8 2.3 Explorer 16開發板硬體架構............................ 10 2.3.1 微處理器(PIC24FJ128GA010, MCU) .................... 11 2.3.2 類比輸入與取樣頻率................................. 14 2.3.3 數位輸出與通訊介面................................. 16 2.4 三軸加速規設定....................................... 20 2.4.1 加速規靈敏度(Sensitivity) ......................... 20 2.4.2 加速規各軸的類比輸出電壓........................... 22 2.4.3 頻寬響應(Bandwidth Response) ...................... 23 2.5 操作介面軟體與通訊協定............................... 24 2.5.1 通訊協定(Protocol)................................. 26 2.5.2 軟體控制介面....................................... 27 2.5.3 量測結果儲存....................................... 28 2.5.4 量測結果單位轉換................................... 29 第三章訊號處理與分析方法................................. 32 3.1 雜訊抑制 ............................................ 32 3.1.1 小波濾波 .......................................... 33 3.1.2 零相位延遲數位濾波器............................... 35 3.2 低通率波(Low pass Filter)............................ 37 3.3 高通率波(High pass Filter)........................... 37 3.4 多變量多尺度熵(Multivariate Multi Scale Entropy, MMSE)41 3.5 多變量多尺度熵指標(Multivariate Multi Scale Entropy Index, MMEI)42 第四章實驗方法與結果..................................... 44 4.1 實驗設計 ............................................ 44 4.1.1 靜態影像實驗設計................................... 47 4.1.2 數學運算實驗設計................................... 47 4.1.3 動態影像實驗設計................................... 48 4.2 實驗結果 ............................................ 49 4.2.1 靜態影像實驗結果................................... 49 4.2.2 數學運算實驗結果................................... 51 4.2.3 動態影像實驗結果....................................53 4.3 MMSE統整分析與比對................................... 55 4.3.1 同一實驗不同部位比對............................... 56 4.3.2 同一部位不同實驗比對............................... 58 第五章結論與未來展望..................................... 61 參考文獻................................................. 62 附錄A-使用手冊 .......................................... 65

    參考文獻
    [1]
    S. A. Fontana and C. M. Porth, "Disorders of Hearing," Pathophysiology: Concepts of altered health states., 2005.
    [2]
    李慧玲, 詹元碩, 不同平衡能力的學齡兒童在視覺與聽覺注意力情境下之探討, 國立臺灣體育大學, 2009.
    [3]
    "Binary file," [Online]. Available: http://en.wikipedia.org/wiki/Binary_file.
    [4]
    "Comma-separated values," [Online]. Available: http://en.wikipedia.org/wiki/Comma-separated_values.
    [5]
    M. Mathie, A. Coster, N. Lovell and B. Celler , "Accelerometry: Providing an integrated, practical method for long-term, ambulatory monitoring of human movement," Physiol. Meas., vol. 25, no. 2, pp. R1 - R20, 2004.
    [6]
    M. Mancini, C. Zampieri, P. Carlson-Kuhta, L. Chiari and F. Horak, "Anticipatory postural adjustments prior to step initiation are hypometric in untreated Parkinson's disease: An accelerometer-based approach," European Journal of Neurology, vol. 16, pp. 1028-1034, 2009.
    [7]
    李
    燕茹, 三度空間加速規與測力板在靜態平衡測量之間的相關性, 國立體育學院, 2007.
    [8]
    M. Dozza, L. Chiari, F. Hlavacka, A. Cappello and F. Horak, "Effects of Linear versus Sigmoid Coding of Visual or Audio Biofeedback for the Control of Upright Stance," IEEE Transactions., pp. 505-512, 2006.
    [9]
    R. E. Mayagoitia, J. C. Lotters, P. H. Veltink and H. Hermens, "Standing balance evaluation using a triaxial accelerometer," Gait Posture, vol. 16, no. 1, pp. 55-59, 2002.
    [10]
    M. U. Ahmed, and D. P. Mandic, "Multivariate Multiscale Entropy Analysis," IEEE Transactions., pp. 91-94, 2012.
    [11]
    N, ±1.5g, ±6g Three Axis Low-g Micromachined Accelerometer Technical Data Rev 0, Freescale Semiconductor, 2008.
    [12]
    N, Explorer 16 Development Board User’s Guide, Microchip Technology Inc, 2005.
    [13]
    N, PIC24FJ128GA010 Family Data Sheet, Microchip Technology Inc, 2009.
    [14]
    Donoho D.L. De-Noising by Soft-Thresholding. Ieee T Inform Theory, 1995, 41(3), pp. 613-627.
    [15]
    Donoho, D.L.; I.M. Johnstone (1994), "Ideal spatial adaptation by wavelet shrinkage," Biometrika, Vol. 81, pp. 425-455.
    63
    [16]
    Donoho, D.L. (1995), "De-noising by soft-thresholding," IEEE Trans. on Inf. Theory, 42 3, pp. 613- 627.
    [17]
    Donoho, D.L.; I.M. Johnstone, G. Kerkyacharian, D. Picard (1995), "Wavelet shrinkage: asymptotia," Jour. Roy. Stat. Soc., series B, Vol. 57, No. 2, pp. 301-369.
    [18]
    K. M. Sanjit, "Digital Signal Processing," 3rd Ed., McGRAW.Hill International Edition, 2006.
    [19]
    M. Costa, A. L. Goldberger and C. K. Peng, " Multiscale entropy analysis of complex physiologic time series," Physical Review Letters, vol. 89, no. 6, 623-656, Aug 5 2002.
    [20]
    M. U. Ahmed and D. P. Mandic, "Multivariate multiscale entropy: A tool for complexity analysis of multichannel data," Physical Review E, vol. 84, Dec 27 2011.
    [21]
    L. Cao, A. Mees, and K. Judd, “Dynamics from multivariate time series,” Phys. D: Nonlinear Phenomena, vol. 121, no. 1–2, pp. 75–88, 1998.
    [22]
    J. S. Richman and J. R. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy,” AJP—Heart Circ.
    Physiol., vol. 278, no. 6, pp. H2039–2049, 2000.
    [23]
    S. Wadhwani, K. A. Wadhwani, P. S. Gupta and V. Kumar, "Detection of Bearing Failure in Rotating Machine Using Adaptive Neuro-Fuzzy Inference System," in Power Electronics, Drives and Energy Systems, 2006. PEDES '06. International Conference on, New Delhi, 2006.
    [24]
    D. Karantonis, M. Narayanan, M. Mathie, N. Lovell and B. Celler, " Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring," IEEE Trans. Inform. Tech. Biomed., pp. 156-167, 2006.
    [25]
    H. Ghasemzadeh, R. Jafari and B. Prabhakaran, "A body sensor network with electromyogram and inertial sensors: Multimodal interpretation of muscular activities," Information Technology in Biomedicine, IEEE Transactions on, vol. 14, pp. 198 -206, 2010.
    [26]
    "mt-moran-grand-teton_51548_990x742-620×464," [Online]. Available: http://beartales.me/2012/05/16/amazing-photos-5/mt-moran-grand-teton_
    51548_990x742-620x464/.
    [27]
    R. S. Kennedy, N. E. Lane, K. S. Berbaum and M. G. Lilienthal, "Simulator sickness questionnaire: An enhanced method for quantifying simulator sickness.," The International Journal of Aviation Psychology,
    vol. 3, no. 3, pp. 203-220, 1993.
    [28]
    "Self-Hypnosis & Optical Illusion," [Online]. Available: http://www.youtube.com/watch?v=Ig0dkKOZTes.

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