Falls are a major concern of public health, particularly for older adults, as the consequences of falls include serious injuries and death. Therefore, the understanding and evaluation of postural control is considered key, as its deterioration is an important risk factor predisposing to falls. In this work we introduce a new Langevin-based model, local recall, that integrates the information from both the center of pressure (CoP) and the center of mass (CoM) trajectories, and compare its accuracy to a previously proposed model that only uses the CoP. Nine healthy young participants were studied under quiet bipedal standing conditions with eyes either open or closed, while standing on either a rigid surface or a foam. We show that the local recall model produces significantly more accurate prediction than its counterpart, regardless of the eyes and surface conditions, and we replicate these results using another publicly available human dataset. Additionally, we show that parameters estimated using the local recall model are correlated with the quality of postural control, providing a promising method to evaluate static balance. These results suggest that this approach might be interesting to further extend our understanding of the underlying mechanisms of postural control in quiet stance.

中文翻译：

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基于 Langevin 的模型，通过移动姿势目标来量化姿势控制


跌倒是公共卫生的一个主要问题，特别是对于老年人来说，因为跌倒的后果包括严重受伤和死亡。因此，对姿势控制的理解和评估被认为是关键，因为姿势控制的恶化是导致跌倒的重要危险因素。在这项工作中，我们引入了一种新的基于 Langevin 的模型，即局部召回，它集成了来自压力中心 (CoP) 和质心 (CoM) 轨迹的信息，并将其准确性与之前提出的仅使用缔约方会议。九名健康的年轻参与者在安静的双足站立条件下进行研究，睁眼或闭眼，站立在刚性表面或泡沫上。我们表明，无论眼睛和表面条件如何，局部召回模型都能比其对应模型产生更准确的预测，并且我们使用另一个公开可用的人类数据集复制这些结果。此外，我们表明使用局部回忆模型估计的参数与姿势控制的质量相关，为评估静态平衡提供了一种有前景的方法。这些结果表明，这种方法可能很有趣，可以进一步扩展我们对安静姿势下姿势控制的潜在机制的理解。