Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning

Maggie Stark, Haikun Huang, Lap Fai Yu, Rebecca Martin, Ryan McCarthy, Emily Locke, Chelsea Yager, Ahmed Ali Torad, Ahmed Mahmoud Kadry, Mostafa Ali Elwan, Matthew Lee Smith, Dylan Bradley, Ali Boolani

Research output: Contribution to journalArticlepeer-review

Abstract

Literature suggests that anxiety affects gait and balance among young adults. However, previous studies using machine learning (ML) have only used gait to identify individuals who report feeling anxious. Therefore, the purpose of this study was to identify individuals who report feeling anxious at that time using a combination of gait and quiet balance ML. Using a cross-sectional design, participants (n = 88) completed the Profile of Mood Survey-Short Form (POMS-SF) to measure current feelings of anxiety and were then asked to complete a modified Clinical Test for Sensory Interaction in Balance (mCTSIB) and a two-minute walk around a 6 m track while wearing nine APDM mobility sensors. Results from our study finds that Random Forest classifiers had the highest median accuracy rate (75%) and the five top features for identifying anxious individuals were all gait parameters (turn angles, variance in neck, lumbar rotation, lumbar movement in the sagittal plane, and arm movement). Post-hoc analyses suggest that individuals who reported feeling anxious also walked using gait patterns most similar to older individuals who are fearful of falling. Additionally, we find that individuals who are anxious also had less postural stability when they had visual input; however, these individuals had less movement during postural sway when visual input was removed.

Original languageEnglish (US)
Article number3163
JournalSensors
Volume22
Issue number9
DOIs
StatePublished - May 1 2022

Keywords

  • APDM monitors
  • anxiety
  • balance
  • gait
  • mCTSIB
  • machine learning
  • sensors

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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