Identifying Anxiety Through Gait, Balance

Can we identify anxiety by a person’s walk? Based on a study conducted by Clarkson students, the answer is yes.

“Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning” explored whether computers can identify anxiety based on a person’s gait. The team’s research was published in the Sensors Special Issue on Analysis of Biomedical Signals and Physical Behavior Sensing in the Development of Systems for Monitoring, Training, Controlling and Improving Quality of Life.

The team — MAGGIE STARK ’19, EMILY LOCKE ’23, RYAN MCCARTHY ’24, postdoctoral scholars AHMED ALI MOHAMED TORAD, AHMED MAHMOUD KADRY TAYEE and MOUSTAFA ALI ELWAN, and SUNY Canton student Dylan Bradley — worked on the project with Adjunct Research Professor ALI BOOLANI.

Each study participant completed a questionnaire to measure their feelings of anxiety. They then completed a balance test and a two-minute walk while wearing sensors.

Based on the data collected, the team determined that young people who report feelings of anxiety