Characterizing the limits of human stability during motion: perturbative experiment validates a model-based approach for the Sit-to-Stand task
Characterizing the limits of human stability during motion: perturbative experiment validates a model-based approach for the Sit-to-Stand task
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DOI：
10.1098/rsos.191410
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发表时间：
2020-01-15
影响因子：
3.5
通讯作者：
Vasudevan, Ram
中科院分区：
文献类型：
--
作者：
Holmes, Patrick D.;Danforth, Shannon M.;Vasudevan, Ram
每年跌倒影响的人口数量不断增加。评估跌倒风险的临床方法通常评估特定运动的表现，例如平衡或坐立。不幸的是，这些技术已被证明预测能力很差，并且无法识别最不稳定的运动部分。为此，识别可以在指定控制策略下完成任务的一组身体配置可能很有用。由此产生的稳定和不稳定运动之间的特定策略边界可用于识别有跌倒风险的个体。最近提出的稳定盆被定义为随着时间的推移，在其选择的控制策略下不会导致个体失败的一组配置。本文提出了一种计算 Stability Basin 的新方法，并通过扰动坐立实验对 Stability Basin 进行了首次实验验证，该实验涉及 11 名受试者通过电机驱动电缆向前或向后拉动。单独构建的稳定盆用于识别试验何时失败，即个体何时必须改变其选择的控制策略（以步骤或坐姿表示）以从扰动中恢复。构建的稳定盆以超过 90% 的准确率正确预测观察到失败的试验结果，并以超过 95% 的准确率正确预测成功试验的结果。将 Stability Basin 与其他三种方法进行比较，发现在所有情况下估计稳定区域的准确度提高了 45% 以上。这项研究表明，稳定盆提供了一种基于模型的新颖方法来量化运动过程中的稳定性，可用于对有跌倒风险的个体进行物理治疗。
Falls affect a growing number of the population each year. Clinical methods to assess fall risk usually evaluate the performance of specific motions such as balancing or Sit-to-Stand. Unfortunately, these techniques have been shown to have poor predictive power, and are unable to identify the portions of motion that are most unstable. To this end, it may be useful to identify the set of body configurations that can accomplish a task under a specified control strategy. The resulting strategy-specific boundary between stable and unstable motion could be used to identify individuals at risk of falling. The recently proposed Stability Basin is defined as the set of configurations through time that do not lead to failure for an individual under their chosen control strategy. This paper presents a novel method to compute the Stability Basin and the first experimental validation of the Stability Basin with a perturbative Sit-to-Stand experiment involving forwards or backwards pulls from a motor-driven cable with 11 subjects. The individually-constructed Stability Basins are used to identify when a trial fails, i.e. when an individual must switch from their chosen control strategy (indicated by a step or sit) to recover from a perturbation. The constructed Stability Basins correctly predict the outcome of trials where failure was observed with over 90% accuracy, and correctly predict the outcome of successful trials with over 95% accuracy. The Stability Basin was compared to three other methods and was found to estimate the stable region with over 45% more accuracy in all cases. This study demonstrates that Stability Basins offer a novel model-based approach for quantifying stability during motion, which could be used in physical therapy for individuals at risk of falling.