根據台灣衛生福利部在2019年的調查結果，65歲以上人口意外死因排行中，跌倒列為第二名。為了有效預防跌倒，衡量姿態穩定性是很重要的一環。Nurwulan et al. (2019) 提出姿態穩定性指標(PSI)以及穩定性尺度，為人們提供一個方法能夠衡量姿態穩定性。
本研究第一個目標為測試Nurwulan et al. (2019)所提出之PSI及穩定性尺度能否應用於其他實際資料，使用的是公開資料集，由行動裝置蒐集的加速度資料。第二個目標為，利用公開資料集的分析結果，對穩定性尺度做修改，讓測使者能夠得到更合適的姿態穩定性建議。第三個目標為，本研究會利用行動裝置蒐集加速度資料，並對於不同年齡、不同速度以及不同每秒步數去做測試，測試哪些因素會影響PSI的值。
分析分法的第一步會先對原始資料做資料前處理，包含建立特徵值以及決定資料點數量，接下來會根據Nurwulan et al. (2019)所提出之PSI建立步驟，執行總體經驗模態分解(EEMD)，計算各本質模態函數(IMF)之多尺度熵(MSE)，決定主要的IMF並建立PSI。最後參照穩定性尺度，則能得到姿態的穩定狀態建議。
本研究的第一個實驗結果為，PSI能夠應用於行動裝置所蒐集之實際加速度資料；第二個結果為，對於年輕人或老年人，PSI均能夠應用於每日的姿態穩定度監測，並得到年齡不是影響PSI值的因素；第三個結果為，經過修改後的穩定性尺度，比起原穩定性尺度能夠給測試者更合適的姿態穩定度建議；第四個結果為，對於同一測試者，不同速度及不同每秒步數會影響PSI的值。

According to Health Promotion Administration, Ministry of Health and Welfare, the second top causes of death of accidents among people over 65 years old is falling in 2019. To prevent people from falling, evaluating human postural stability is an important issue. Nurwulan et al. (2019) proposed the postural stability index (PSI) and stability scales to provide a way for human to evaluate their stability of postures.
The first objective of this study is to apply the PSI and stability scales to open datasets, to see if the way to evaluate human postural stability is useful for other real data. Based on the result of the first objective, the second objective is to revise the original stability scales to give the users more suitable suggestions. The third objective is to collect acceleration data with mobile devices to test if different ages, velocity and step counts per second will affect the PSI value.
Each dataset used in this study is acceleration data. The first step is doing the data preprocessing. The second step is building the PSI, including running the ensemble empirical mode decomposition (EEMD), calculating the multiscale entropy (MSE), and determining the dominant intrinsic mode function (IMF). Then referring to the stability scales to check the status of posture.
The first result of this study is that the PSI can be applied to acceleration data collected by mobile devices. The second one is that the PSI can be applied to daily monitor of postural stability for both young and elder, and the PSI is not affected by different ages. The third one is the revised stability scales can be applied to acceleration data collected in real life more suitably than the original one. And the last one is different velocity and step counts per second will affect the PSI value.

Aminian, K., Robert, P., Buchser, E., Rutschmann, B., Hayoz, D., & Depairon, M. (1999). Physical activity monitoring based on accelerometry: validation and comparison with video observation. Medical & biological engineering & computing, 37(3), 304-308.
Bachmann, H., Ammann, W. J., Deischl, F., Eisenmann, J., Floegl, I., Hirsch, G. H., Klein, G. K., Lande, G. J., Mahrenholtz, O., Natke, H. G., Nussbaumer, H., Pretlove, A. J., Rainer J. H., Saemann, E.-U., Steinbeisser L. (1995). Vibration problems in structures: Practical guidelines: Springer science & business media.
Bao, L., & Intille, S. S. (2004). Activity recognition from user-annotated acceleration data. Paper presented at the International Conference on Pervasive Computing.
Chaudhry, H., Findley, T., Quigley, K. S., Bukiet, B., Ji, Z., Sims, T., & Maney, M. (2004). Measures of postural stability. Journal of rehabilitation research & development, 41(5).
Costa, M., Goldberger, A. L., & Peng, C.-K. (2002). Multiscale entropy analysis of complex physiologic time series. Physical review letters, 89(6), 068102.
Cui, X., Peng, C.-K., Costa, M. D., Weiss, A., Goldberger, A. L., & Hausdorff, J. M. (2014). Development of a new approach to quantifying stepping stability using ensemble empirical mode decomposition. Gait & posture, 39(1), 495-500.
Dai, J., Bai, X., Yang, Z., Shen, Z., & Xuan, D. (2010). Mobile phone-based pervasive fall detection. Personal and ubiquitous computing, 14(7), 633-643.
Dernbach, S., Das, B., Krishnan, N. C., Thomas, B. L., & Cook, D. J. (2012). Simple and complex activity recognition through smart phones. Paper presented at the 2012 Eighth International Conference on Intelligent Environments.
Dimeas, F., & Aspragathos, N. (2016). Online stability in human-robot cooperation with admittance control. IEEE transactions on haptics, 9(2), 267-278.
Fino, P. C., Mojdehi, A. R., Adjerid, K., Habibi, M., Lockhart, T. E., & Ross, S. D. (2016). Comparing postural stability entropy analyses to differentiate fallers and non-fallers. Annals of biomedical engineering, 44(5), 1636-1645.
Hasan, S. S., Robin, D. W., Szurkus, D. C., Ashmead, D. H., Peterson, S. W., & Shiavi, R. G. (1996). Simultaneous measurement of body center of pressure and center of gravity during upright stance. Part I: Methods. Gait & posture, 4(1), 1-10.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. E., Tung, C. C., Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the royal society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903-995.
Huang, N. E., Wu, Z., Long, S. R., Arnold, K. C., Chen, X., & Blank, K. (2009). On instantaneous frequency. Advances in adaptive data analysis, 1(02), 177-229.
Inman, V. T., & Eberhart, H. D. (1953). The major determinants in normal and pathological gait. Jouranal of bone and joint surgery (JBJS), 35(3), 543-558.
Ji, T., & Pachi, A. (2005). Frequency and velocity of people walking. Structural engineer, 84(3), 36-40.
Lee, S.-W., & Mase, K. (2002). Activity and location recognition using wearable sensors. IEEE pervasive computing, 1(3), 24-32.
Lei, Y., Li, N., Lin, J., & Wang, S. (2013). Fault diagnosis of rotating machinery based on an adaptive ensemble empirical mode decomposition. Sensors, 13(12), 16950-16964.
Matsumoto, Y. (1978). Dynamic design of footbridges.
Morris, J. (1973). Accelerometry—A technique for the measurement of human body movements. Journal of biomechanics, 6(6), 729-736.
Nurwulan, N. R., Jiang, B. C., & Novak, V. (2019). Development of postural stability index to distinguish different stability states. Entropy, 21(3), 314.
Peters, S. C., & Iagnemma, K. (2006). An analysis of rollover stability measurement for high-speed mobile robots. Paper presented at the Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.
Sposaro, F., & Tyson, G. (2009). iFall: an Android application for fall monitoring and response. Paper presented at the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
Winter, D. A. (1995). Human balance and posture control during standing and walking. Gait & posture, 3(4), 193-214.
Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in adaptive data analysis, 1(01), 1-41.
Yang, W.H. (2010). Data mining on physiological signal-integrate dissimilarity approach and signal reconstruction for complexity analysis. (Unpublished doctoral dissertation). Yuan Ze University, Chungli, Taiwan.
Zhang, J., Sun, L., Peng, P., Zhang, X., & Li, J. (2013). Simulation analysis of passive adaptive robot's tip-over stability. Paper presented at the 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO).