Automatic analysis of human posture equilibrium using empirical mode decomposition
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The present study proposes a new approach for the assessment of the human balance control. This approach is based on the decomposition of the center of pressure displacement using empirical mode decomposition (EMD) that provides an effective time-frequency analysis of non-stationary signals. Twenty-eight healthy subjects performed quiet standing in four conditions—feet apart/together with respect to eyes open/closed—while recording the stabilometric signals in the anteroposterior (AP) and mediolateral (ML) directions. The EMD method decomposes each stabilometric signal into several subsignals called intrinsic mode functions (IMFs). Stabilogram-diffusion analysis technique is applied to generate the diffusion curve of each IMF signal. Each diffusion curve is modeled as a second-order system and provides representative features, such as the gain parameter. Analysis of the gain parameter shows the major effect of visual input and feet conditions on the strategy to control/stabilize the balance. Significant differences were found between young and elderly, and between women and men. In addition, the impact of feet position seems to be higher in ML direction than in AP direction.
KeywordsBalance analysis Posture Stabilometric signals Empirical mode decomposition
- 8.Khasnis, A., Gokula, R.M., et al.: Rombergs test. J. Postgrad. Med. 49(2), 169 (2003)Google Scholar
- 18.Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 454, pp. 903–995. The Royal Society, London (1998)Google Scholar
- 24.Guo, J., Qin, S., Zhu, C.: The application of energy operator demodulation approach based on emd in mechanical system identification. In 2012 19th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), pp. 80–85. IEEE (2012)Google Scholar