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Article

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

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Department of Medicine, Lake Erie Osteopathic College of Medicine, Elmira, NY 14901, USA
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Department of Computer Science, George Mason University, Fairfax, VA 22030, USA
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Department of Physical Therapy, Hanover College, Hanover, IN 47243, USA
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Department of Biology, Clarkson University, Potsdam, NY 13699, USA
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Department of Psychology, Clarkson University, Potsdam, NY 13699, USA
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Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY 13699, USA
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Department of Neurology, St. Joseph’s Hospital Health Center, Syracuse, NY 13203, USA
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Department of Physical Therapy, Clarkson University, Potsdam, NY 13699, USA
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Faculty of Physical Therapy, Kafrelsheik University, Kafr El Sheik 33516, Egypt
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Faculty of Physical Therapy, Beni-Suef University, Beni-Suef 62521, Egypt
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Department of Environmental and Occupational Health, School of Public Health, Texas A&M University, College Station, TX 77843, USA
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Canino School of Engineering Technology, State University of New York, Canton, NY 13617, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Michał Strzelecki and Adam Wojciechowski
Sensors 2022, 22(9), 3163; https://doi.org/10.3390/s22093163
Received: 12 January 2022 / Revised: 6 April 2022 / Accepted: 18 April 2022 / Published: 20 April 2022
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. View Full-Text
Keywords: anxiety; gait; mCTSIB; balance; sensors; APDM monitors; machine learning anxiety; gait; mCTSIB; balance; sensors; APDM monitors; machine learning
MDPI and ACS Style

Stark, M.; Huang, H.; Yu, L.-F.; Martin, R.; McCarthy, R.; Locke, E.; Yager, C.; Torad, A.A.; Kadry, A.M.; Elwan, M.A.; Smith, M.L.; Bradley, D.; Boolani, A. Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning. Sensors 2022, 22, 3163. https://doi.org/10.3390/s22093163

AMA Style

Stark M, Huang H, Yu L-F, Martin R, McCarthy R, Locke E, Yager C, Torad AA, Kadry AM, Elwan MA, Smith ML, Bradley D, Boolani A. Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning. Sensors. 2022; 22(9):3163. https://doi.org/10.3390/s22093163

Chicago/Turabian Style

Stark, Maggie, Haikun Huang, Lap-Fai Yu, Rebecca Martin, Ryan McCarthy, Emily Locke, Chelsea Yager, Ahmed A. Torad, Ahmed M. Kadry, Mostafa A. Elwan, Matthew L. Smith, Dylan Bradley, and Ali Boolani. 2022. "Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning" Sensors 22, no. 9: 3163. https://doi.org/10.3390/s22093163

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