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

Maggie Stark1, Haikun Huang2, Lap-Fai Yu2

  • 1Department of Medicine, Lake Erie Osteopathic College of Medicine, Elmira, NY 14901, USA.

Sensors (Basel, Switzerland)
|May 20, 2022
PubMed
Summary

Anxiety in young adults impacts gait and balance. Machine learning models accurately identified anxious individuals using gait and balance data, revealing gait patterns similar to those fearful of falling.

Keywords:
APDM monitorsanxietybalancegaitmCTSIBmachine learningsensors

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Area of Science:

  • Biomechanics
  • Psychology
  • Machine Learning

Background:

  • Anxiety is known to affect gait and balance in young adults.
  • Previous machine learning studies primarily used gait data to detect anxiety.

Purpose of the Study:

  • To identify anxious individuals using a combination of gait and quiet balance machine learning.
  • To explore the relationship between anxiety, gait parameters, and balance control.

Main Methods:

  • A cross-sectional study with 88 participants.
  • Utilized the Profile of Mood Survey-Short Form (POMS-SF) for anxiety assessment.
  • Employed a modified Clinical Test for Sensory Interaction in Balance (mCTSIB) and a 2-minute walk with APDM mobility sensors.

Main Results:

  • Random Forest classifiers achieved a median accuracy of 75% in identifying anxious individuals.
  • Top predictive features included gait parameters: turn angles, neck/lumbar rotation variance, sagittal lumbar movement, and arm movement.
  • Anxious individuals exhibited gait patterns resembling older adults fearful of falling.

Conclusions:

  • Machine learning effectively identifies anxiety in young adults using combined gait and balance metrics.
  • Anxiety is associated with altered gait patterns and reduced postural stability, particularly with visual input.
  • The study highlights the potential of wearable sensor technology for objective anxiety assessment.