Influence of simulated neuromuscular noise on the dynamic stability and fall risk of a 3D dynamic walking model.
Influence of simulated neuromuscular noise on the dynamic stability and fall risk of a 3D dynamic walking model.
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DOI：
10.1016/j.jbiomech.2011.03.003
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发表时间：
2011-05-17
影响因子：
2.4
通讯作者：
Dingwell, Jonathan B.
中科院分区：
文献类型：
--
作者：
Roos, Paulien E.;Dingwell, Jonathan B.
可以预测跌倒风险的措施对于老年人参加跌倒预防计划至关重要。局部稳定性和轨道稳定性直接量化了对非常小的扰动的反应，因此被认为是预测坠落风险的候选对象。然而，迄今为止的研究还没有确定这些措施是否以及如何与跌倒风险相关。凭经验对此进行测试将是耗时的，或者可能需要高风险的跳闸实验。因此，模拟研究提供了一个重要的工具，初步探索潜在的措施，以预测跌倒风险。本研究使用3D动态步行模型进行模拟，以探索动态稳定性措施是否以及如何预测跌倒风险。该模型采用横向步进控制器来保持横向稳定性。增加幅度的神经元噪声被添加到该控制器中以操纵跌倒风险。短期（）局部不稳定性确实能预测跌倒风险，但长期（）局部不稳定性和眼眶稳定性（maxFM）不能。此外，本发明还 是跌倒风险的早期预测因素，因为它在跌倒风险增加之前就开始增加了。因此，我们认为， 这是一个非常有用的工具，可以用来识别那些跌倒风险即将增加的老年人，这样他们就可以在跌倒之前参加预防跌倒的计划。
Measures that can predict risk of falling are essential for enrollment of older adults into fall prevention programs. Local and orbital stability directly quantify responses to very small perturbations and are therefore putative candidates for predicting fall risk. However, research to date is not conclusive on whether and how these measures relate to fall risk. Testing this empirically would be time consuming or may require high risk tripping experiments. Simulation studies therefore provide an important tool to initially explore potential measures to predict fall risk. This study performed simulations with a 3D dynamic walking model to explore if and how dynamic stability measures predict fall risk. The model incorporated a lateral step controller to maintain lateral stability. Neuronal noise of increasing amplitude was added to this controller to manipulate fall risk. Short-term ( ) local instability did predict fall risk, but long-term ( ) local instability and orbital stability (maxFM) did not. Additionally, was an early predictor for fall risk as it started increasing before fall risk increased. Therefore, could be a very useful tool to identify older adults whose fall risk is about to increase, so they can be enrolled in fall prevention programs before they actually fall.
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影响因子：
2.4
作者：
Dingwell, Jonathan B.;Kang Hyun Gu;Marin, Laura C.
通讯作者：
Marin, Laura C.
影响因子：
9.2
作者：
Kuo, AD
通讯作者：
Kuo, AD
影响因子：
2.5
作者：
O'Connor, Shawn M.;Kuo, Arthur D.
通讯作者：
Kuo, Arthur D.
影响因子：
2.4
作者：
Donelan, JM;Shipman, DW;Kuo, AD
通讯作者：
Kuo, AD
影响因子：
2.4
作者：
Dingwell, JB;Marin, LC
通讯作者：
Marin, LC