New research by experts at Clarkson University set out to establish if the use of electronic machines can identify if an individual is anxious simply based on their walking habits.
The study, released in Sensors, involved a cross-sectional design, with as many as 88 participants being administered a modified Clinical Test for Sensory Interaction in Balance (mCTSIB).
The participants were also instructed to undergo a modified Clinical Test for Sensory Interaction in Balance (mCTSIB) and a two-minute walk while wearing APDM sensors.
“To the knowledge of the researchers, this is the first study to utilize sensors and machine learning to identify current feelings of anxiety using gait and balance measures.” the authors of the study explain.
“Although the accuracy of the models in this study is aligned with previously reported literature that identified feelings of anxiety over the last 2 weeks, the findings of this study add significantly to the literature by reporting gait characteristics that have clinical meaning and measures that can be used to identify individuals who currently report being anxious,” the authors concluded.
“The findings from these analyses suggest that the turning angle, mean lumbar movement, and variations in the neck and arm movements are the most important features in predicting anxiety.”