Digital Human Research Center, National Institute of Advanced Industrial Science and Technology, 2-8-5 Aomi, Koto-ku, Tokyo 135-0064, Japan.
Digital Human Research Center, National Institute of Advanced Industrial Science and Technology, 2-8-5 Aomi, Koto-ku, Tokyo 135-0064, Japan.
J Biomech. 2014 Jul 18;47(10):2424-9. doi: 10.1016/j.jbiomech.2014.04.011. Epub 2014 Apr 16.
It has been reported that fallers have a higher risk of subsequent falls than non-fallers. Therefore, if the differences between the movements of recent fallers and non-fallers can be identified, such could be regarded as the basis of the high risk of falling of the former. The objective of the present study was the identification of the key joint kinematic characteristics of human gait related to the risk of falling while walking on level ground. For this purpose, joint kinematics data obtained from 18 recent fallers and 19 non-fallers were analyzed using principal component analysis (PCA). The PCA was conducted using an input matrix constructed from the time-normalized average and standard deviation of the lower limb joint angles on three planes (101 data×2 parameters×3 angles×3 planes). The PCA revealed that only the 5th principal component vector (PCV 5) among the 23 generated PCVs was related to the risk of falling (p<0.05, ES=0.71). These findings as well as those of previous studies suggest that the joint kinematics of PCV 5 is the key characteristic that affects the risk of falling while walking. We therefore recombined the joint kinematics corresponding to PCV 5 and concluded that the variability of the joint kinematics for fallers was larger than that for non-fallers regardless of the joint. These observations as well as the findings of previous studies suggest that the risk of falling can be reduced by reducing the variability of the joint kinematics using an intervention such as external cues or a special garment.
据报道，跌倒者比非跌倒者有更高的再次跌倒风险。因此，如果能识别出近期跌倒者和非跌倒者的运动差异，这些差异就可以被视为前者跌倒风险高的原因。本研究的目的是确定与平地行走时跌倒风险相关的人体步态关键关节运动学特征。为此，使用主成分分析（PCA）对 18 名近期跌倒者和 19 名非跌倒者的关节运动学数据进行了分析。PCA 是使用构建于三个平面上（101 个数据×2 个参数×3 个角度×3 个平面）下肢关节角度的时间归一化平均值和标准差的输入矩阵进行的。PCA 结果表明，在生成的 23 个主成分向量（PCV）中，只有第 5 个主成分向量（PCV5）与跌倒风险有关（p<0.05，ES=0.71）。这些发现与之前的研究结果表明，PCV5 的关节运动学是影响步行时跌倒风险的关键特征。因此，我们重新组合了与 PCV5 对应的关节运动学，并得出结论，无论关节如何，跌倒者的关节运动学变异性均大于非跌倒者。这些观察结果以及之前的研究结果表明，可以通过使用外部提示或特殊服装等干预措施来减少关节运动学的变异性，从而降低跌倒风险。
