Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/90435
標題: Gait feature extraction and EMD in health population using accelerometers
應用EMD於健康人士的步態加速度訊號分析
作者: 毛士豪
Shih-Hao Mao
關鍵字: 三維加速度規
經驗模態分解法
峰頂檢測法
tri-axial accelerometers
empirical mode decomposition
peak detection
引用: [1] Sequeira, M. M., Rickenbach, M., Wietlisbach, V., Tullen, B., & Schutz, Y. (1995). Physical activity assessment using a pedometer and its comparison with a questionnaire in a large population survey. American Journal of Epidemiology, 142(9), pp. 989-999. [2] BenAbdelkader, C., Cutler, R., Nanda, H., & Davis, L. (2001, January). Eigengait: Motion-based recognition of people using image self-similarity. InAudio-and Video-Based Biometric Person Authentication, pp. 284-294. [3] Lam, T. H., & Lee, R. S. (2005). A new representation for human gait recognition: Motion silhouettes image (msi). In Advances in Biometrics, pp. 612-618. [4] Chen, K. Y., & Bassett, D. R. (2005). The technology of accelerometry-based activity monitors: current and future. Medicine and science in sports and exercise, 37(11), S490, pp. 490-500. [5] Sprager, S., & Zazula, D. (2009). A cumulant-based method for gait identification using accelerometer data with principal component analysis and support vector machine. WSEAS Transactions on Signal Processing, 5(11), pp. 369-378. [6] Iso, T., & Yamazaki, K. (2006, September). Gait analyzer based on a cell phone with a single three-axis accelerometer. In Proceedings of the 8th conference on Human-computer interaction with mobile devices and services, pp. 141-144. [7] Annadhorai, A., Guenterberg, E., Barnes, J., Haraga, K., & Jafari, R. (2008, June). Human identification by gait analysis. In Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments, pp. 11 [8] Lee, C. Y., & Lee, J. J. (2002). Estimation of walking behavior using accelerometers in gait rehabilitation. [9] Mantyjarvi, J., Lindholm, M., Vildjiounaite, E., Makela, S. M., & Ailisto, H. A. (2005, March). Identifying users of portable devices from gait pattern with accelerometers. In Acoustics, Speech, and Signal Processing, 2005. Proceedings.(ICASSP'05). IEEE International Conference on Vol. 2, pp. ii-973. [10] Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C. and Liu, H. H., 1998. The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proceedings of Royal Society London. A, No. 454, pp. 903-905. [11] Weng, B., Blanco-Velasco, M., & Barner, K. E. (2006, August). ECG denoising based on the empirical mode decomposition. In Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE, pp. 1-4. [12] Khan, J. F., Adhami, R. R., Bhuiyan, S. M., & Barner, K. E. (2008, March). Empirical mode decomposition based interest point detector. In Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on, pp. 1317-1320. [13] Sprager, S., & Zazula, D. (2009, November). Gait identification using cumulants of accelerometer data. In 2nd WSEAS International Conference on Sensors, and Signals and Visualization, Imaging and Simulation and Materials Science, pp. 94-99. [14] Gafurov, D., Helkala, K., & Sondrol, T. (2006). Biometric gait authentication using accelerometer sensor. Journal of computers, 1(7), pp. 51-59. [15] Peng, C. K., Hausdorff, J. M., & Goldberger, A. L. (2000). Fractal mechanisms in neuronal control: human heartbeat and gait dynamics in health and disease. Self-organized biological dynamics and nonlinear control, Cambridge University Press, Cambridge, pp. 66-96. [16] Kuchi, P., & Panchanathan, S. (2003). Gait recognition using empirical mode decomposition. Advances in Pattern Recognition ICAPR2003, 340. [17] Ibrahim, R. K., Ambikairajah, E., Celler, B. G., & Lovell, N. H. (2008, August). Gait pattern classification using compact features extracted from intrinsic mode functions. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp. 3852-3855. [18] Guan-Sheng Huang, Chao-Cheng Wu, and Jiannher Lin. (2012). Gait Analysis by Using Tri-Axial Accelerometer of Smart Phones. Computerized Healthcare (ICCH) International Conference, pp. 29-34. [19] Marschollek, M., Goevercin, M., Wolf, K. H., Song, B., Gietzelt, M., Haux, R., & Steinhagen-Thiessen, E. (2008, August). A performance comparison of accelerometry-based step detection algorithms on a large, non-laboratory sample of healthy and mobility-impaired persons. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp. 1319-1322. [20] Tumkur, K., & Subbiah, S. (2012, September). Modeling Human Walking for Step Detection and Stride Determination by 3-Axis Accelerometer Readings in Pedometer. In Computational Intelligence, Modelling and Simulation (CIMSiM), 2012 Fourth International Conference on, pp. 199-204. [21] Frei, M. G., & Osorio, I. (2007). Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals.Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science, 463(2078), pp. 321-342. [22] Rilling, G., & Flandrin, P. (2008). One or two frequencies? The empirical mode decomposition answers. Signal Processing, IEEE Transactions on, 56(1), pp. 85-95. [23] Feldman, M. (2009). Analytical basics of the EMD: two harmonics decomposition. Mechanical Systems and Signal Processing, 23(7), pp. 2059-2071. [24] Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis, 1(01), pp. 1-41. [25] Zhao, N. (2010). Full-featured pedometer design realized with 3-Axis digital accelerometer. Analog Dialogue, 44(06).
摘要: The purpose of this study uses empirical mode decomposition to analysis gait feature extraction at different positions by tri-axial accelerometers. Thirty health young persons(age: 22.27±1.57) are experimental subjects in this study and the sample rate of tri-axial accelerometer (SCA3100-D07) is 200 Hz. All the subjects were told to wear the accelerometers at five specific positions of body include necklaces, arm, chest pocket, pants pocket and waist and simultaneously collect the accelerometer data to use empirical mode decomposition. They are told to walk 40 steps on the ground by metronome at different rate including 60, 80, 100 and 120 steps per minutes. Analysis gait feature extraction by intrinsic mode functions and select the maximum power of IMFs and count steps by the peak detection. The accuracy of five wearing positions at walking rate 80, 100 and 120 (steps/min) are up to 90 percent. By t-test between actual and detected steps, there is the significant difference at the rate 60 (steps/min) (p = 0.041). There are no significant difference at the rate 80, 100, 120 (steps/min) (p = 0.241、p = 0.276、p = 0.834).
本研究主要以經驗模態分解(Empirical Mode Decomposition, EMD),分析三維加速度規於不同位置佩戴之步態訊號。以30位健康年輕男性(22.27±1.57歲)為實驗對象,將三維加速度規(SCA3100-D07)取樣頻率設定200 Hz。讓受測者同時佩戴於胸口項鍊、胸口口袋、右側手臂、右側褲子口袋及右側腰間,蒐集行走於水泥平地四十步的加速度訊號,且過程中配合節拍器以不同步行頻率60、80、100和120(步/分鐘)進行實驗,以三軸之等效合量應用EMD處理訊號,分析本質模態函數(Intrinsic Mode Function, IMF)與步態的關係,其挑選能量最高的IMF分量結合峰頂檢測法計算步數。 結果在步頻80、100和120(步/分鐘)的五個位置步數準確度達到90%以上,以t-test檢定步數準確度,在步頻60(步/分鐘)為有顯著差異(p = 0.041),而步頻80、100、120(步/分鐘)則無顯著差異(p = 0.241、p = 0.276、p = 0.834)。
URI: http://hdl.handle.net/11455/90435
文章公開時間: 2017-07-07
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