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

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.

Identifying Current Feelings of Mild and Moderate to High Depression in Young, Healthy Individuals Using Gait and Balance: An Exploratory Study

“…The OpalTM monitors were attached to the lumbar region (5th lumbar vertebrae), sternum (body of sternum immediately superior to the xyphoid process), forehead middle of the frontal bone (approximately 2.5 cm above the nasal bone), right and left foot (on the metatarsals, directly superior to the metatarsophalangeal joint), and right and left wrist (immediately superior to the radioulnar joint) using VelcroTM straps [ 33 , 34 ]. Interested readers may consult our previously published studies for a detailed description of the gait assessment methodology [ 35 , 36 ]. Gait variables included but were not limited to anticipatory postural adjustment measures, cadence, gait speed, spatiotemporal variables of various gait cycle metrics, and planar angles of the neck, lumbar, leg, and foot segments in three dimensions among others (a list of all variables may be found in the Supplementary Materials and definitions of these variables can be found at , accessed on 23 April 2023)…”

“…A comprehensive account of the pre-processing steps and calculation of gait variables is detailed in our previous publications [ 35 , 36 ]. In brief, the mood surveys obtained from surveymonkey.com were downloaded and scored using Microsoft Excel (Microsoft Inc., Redmond, WA, USA).…”

Detecting Psychological Interventions Using Bilateral Electromyographic Wearable Sensors

“…The dual-task paradigm for modifying gait and muscle activity pattern focuses on disrupting various motor and cognitive processes, including attention to the environment and neurobiomechanical control of the trunk and limbs during locomotion [ 13 , 14 , 15 ]. These findings [ 13 , 14 , 15 ], combined with the works of various scholars, on the effects of various moods, such as anxiety [ 16 , 17 ], depression [ 18 , 19 , 20 ], anger [ 21 ], and fatigue [ 22 , 23 , 24 , 25 ], suggest that psychological conditions can disrupt motor coordination patterns.…”

“…Therefore, to assess the impact of psychological interventions on muscle activation patterns, the authors of this study chose to implement auditory psychological interventions on a dynamic movement, specifically walking gait. Due to the limited intra-individual variability of walking gait [ 6 , 7 , 8 ] and previous work supporting the impact of affect on modifying walking gait [ 16 , 17 , 18 , 19 , 20 , 21 ], the authors chose to use walking gait to assess the impact of various psychological interventions on walking gait muscle firing patterns. Also, based on the previous works of these authors on the impact of music, silence, positive, and negative feedback on modifying basketball jump shooting performance [ 28 ], the authors chose to use these four interventions to modify muscle activation patterns.…”