Department of Physics, University of Helsinki, FI-00014, Helsinki, Finland.
School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK.
Sci Rep. 2017 Jun 19;7(1):3771. doi: 10.1038/s41598-017-02372-1.
The control of the human body sway by the central nervous system, muscles, and conscious brain is of interest since body sway carries information about the physiological status of a person. Several models have been proposed to describe body sway in an upright standing position, however, due to the statistical intractability of the more realistic models, no formal parameter inference has previously been conducted and the expressive power of such models for real human subjects remains unknown. Using the latest advances in Bayesian statistical inference for intractable models, we fitted a nonlinear control model to posturographic measurements, and we showed that it can accurately predict the sway characteristics of both simulated and real subjects. Our method provides a full statistical characterization of the uncertainty related to all model parameters as quantified by posterior probability density functions, which is useful for comparisons across subjects and test settings. The ability to infer intractable control models from sensor data opens new possibilities for monitoring and predicting body status in health applications.
人体由中枢神经系统、肌肉和有意识的大脑控制，身体的摆动携带有关人体生理状态的信息，因此对其进行控制具有重要意义。已经提出了几种模型来描述直立站立位置的身体摆动，但是，由于更现实的模型在统计学上难以处理，因此以前没有进行正式的参数推断，并且这些模型对于真实人体受试者的表达能力仍然未知。我们使用最新的贝叶斯统计推断方法来处理难以处理的模型，为足动描记术测量值拟合了一个非线性控制模型，结果表明，该模型可以准确地预测模拟和真实受试者的摆动特征。我们的方法为与所有模型参数相关的不确定性提供了完整的统计描述，这些参数通过后验概率密度函数进行量化，这对于在不同的受试者和测试设置之间进行比较非常有用。从传感器数据推断难以处理的控制模型为健康应用中的身体状态监测和预测开辟了新的可能性。
