Chou Li-Wei, Chang Kang-Ming, Wei Yi-Chun, Lu Mei-Kuei
Department of Physical Medicine and Rehabilitation, China Medical University Hospital, Taichung City 40402, Taiwan.
Department of Physical Medicine and Rehabilitation, Asia University Hospital, Asia University, Taichung City 41354, Taiwan.
Entropy (Basel). 2021 Apr 16;23(4):472. doi: 10.3390/e23040472.
Fall risk prediction is an important issue for the elderly. A center of pressure signal, derived from a force plate, is useful for the estimation of body calibration. However, it is still difficult to distinguish elderly people's fall history by using a force plate signal. In this study, older adults with and without a history of falls were recruited to stand still for 60 s on a force plate. Forces in the x, y and z directions (Fx, Fy, and Fz) and center of pressure in the anteroposterior (COPx) and mediolateral directions (COPy) were derived. There were 49 subjects in the non-fall group, with an average age of 71.67 (standard derivation: 6.56). There were also 27 subjects in the fall group, with an average age of 70.66 (standard derivation: 6.38). Five signal series-forces in x, y, z (Fx, Fy, Fz), COPX, and COPy directions-were used. These five signals were further decomposed with empirical mode decomposition (EMD) with seven intrinsic mode functions. Time domain features (mean, standard derivation and coefficient of variations) and entropy features (approximate entropy and sample entropy) of the original signals and EMD-derived signals were extracted. Results showed that features extracted from the raw COP data did not differ significantly between the fall and non-fall groups. There were 10 features extracted using EMD, with significant differences observed among fall and non-fall groups. These included four features from COPx and two features from COPy, Fx and Fz.
跌倒风险预测对于老年人来说是一个重要问题。源自测力板的压力中心信号，对于身体校准的估计很有用。然而，利用测力板信号来区分老年人的跌倒史仍然很困难。在本研究中，招募了有跌倒史和无跌倒史的老年人，让他们在测力板上静止站立60秒。得出了x、y和z方向的力（Fx、Fy和Fz）以及前后方向（COPx）和内外侧方向（COPy）的压力中心。非跌倒组有49名受试者，平均年龄为71.67（标准差：6.56）。跌倒组也有27名受试者，平均年龄为70.66（标准差：6.38）。使用了五个信号序列——x、y、z方向的力（Fx、Fy、Fz）、COPX和COPy方向。这五个信号通过具有七个本征模函数的经验模态分解（EMD）进一步分解。提取了原始信号和EMD导出信号的时域特征（均值、标准差和变异系数）和熵特征（近似熵和样本熵）。结果表明，从原始COP数据中提取的特征在跌倒组和非跌倒组之间没有显著差异。使用EMD提取了10个特征，在跌倒组和非跌倒组之间观察到显著差异。这些特征包括来自COPx的四个特征、来自COPy、Fx和Fz的两个特征。
