Linear Langevin-Based Models Providing Predictive Descriptive Statistics for Postural Sway

Yuta Tawaki, Takuichi Nishimura, Toshiyuki Murakami

研究成果: Article査読

抄録

Elderly and disabled people frequently experience falls that may require early assessment and training. The quiet standing test provides descriptive statistics such as the center of pressure (COP) sway data, which can be used to analyze the decline in balance ability. Generally, higher descriptive statistics indicate lower balance ability. Stochastic models can model COP trajectories, and such equation parameters were previously used to assess the balance of patients. However, the model equation is a hypothesis and should be verified. In this study, we evaluated whether stochastic models can predict the descriptive statistics of observed COP trajectories. We estimated the model parameters by fitting postural sway data from 49 individuals in four linear stochastic models, and the prediction accuracy was verified by comparing the observed descriptive statistics with the predicted COP trajectories. We observed that the prediction accuracy of the models with stiffness was much higher than those with viscosity only, and the models with the center of mass (COM) had higher prediction accuracy than COP-only models. Therefore, we identified that coefficients from Langevin-based models contained descriptive statistics about a subject's COP trajectory, which suggested that these coefficients can be used to effectively assess balance issues.

本文言語English
論文番号9514503
ページ(範囲)114485-114494
ページ数10
ジャーナルIEEE Access
9
DOI
出版ステータスPublished - 2021

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 材料科学(全般)
  • 工学(全般)

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