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

Yuta Tawaki, Takuichi Nishimura, Toshiyuki Murakami

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number9514503
Pages (from-to)114485-114494
Number of pages10
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • Center of pressure (COP)
  • Langevin model
  • postural control
  • postural sway
  • prediction accuracy
  • quiet standing

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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