UT Dallas researchers using body movement to predict mental health crises

Researchers at the University of Texas at Dallas are exploring a groundbreaking way to detect mental health crises before they happen by analyzing how people move.

Gu Eon Kang, PhD, an assistant bioengineering professor at UTD, is leading a team of student researchers to identify markers for depression, anxiety, and psychological distress through physical motion.

The "Hallmark" of Movement

While many might assume a mental health struggle would simply result in a slower walking pace, researchers found that the indicators are much more subtle.

"When you think of someone who's sad, I was really expecting people who walked slower," said doctoral student Angeloh Stout. "In doing my analysis, the big hallmark is all about your posture... not necessarily how fast you walk."

Professor Kang demonstrated that jerky or stiff movements—such as an elbow extension that isn't smooth—are significant indicators of a potential crisis.

Inside the Lab

The team uses a sophisticated setup to capture this data:

• Reflective Markers: Small, thimble-like nodules are attached to a subject's skin and clothing.

• Infrared Cameras: Cameras surrounding the lab bounce light off the markers to track precise movement.

• Complex Data: Computers record intricate measurements as participants walk across platforms or stand up from a chair.

By comparing this computer data to participants' self-assessments, the team found high levels of accuracy. A standard straight walk predicted anxiety or depression with 75% accuracy, while the "sit to walk" movement increased that accuracy to 78%.

A Tool for the Future

The goal of the research is to provide technology-based solutions for a demographic that needs it most. Stout noted that statistics show nearly half of college students experience depression or anxiety.

"Now we are exploring a way to predict suicidal thoughts, especially in college students, just based on body movement," Professor Kang explained. "We want to help people, and we want to save people."

The team envisions a future where wearable or in-home devices could flag a mental health risk and prompt someone to seek care—perhaps even triggering soothing music if a risk is detected during a morning routine.

While the research is not intended to replace professional mental health care, the team believes it can become a vital tool for early intervention.

"It works," said Professor Kang. "It works pretty well."