Advances in motion capture technology and machine learning could play a key role in identifying mental health conditions like depression and anxiety, according to new research from the University of Texas at Dallas.
The study, published in the May issue of Gait & Posture, reveals how analyzing a person’s movement, such as the way they walk or stand up from a chair, could help detect symptoms of mental illness. The research shows that, by using 3D motion capture and machine-learning models, it is possible to spot signs of depression and anxiety based on physical movement patterns.
Dr. Gu Eon Kang, Assistant Professor of Bioengineering at the Erik Jonsson School of Engineering and Computer Science, led the study and emphasized the potential for using gait analysis to objectively assess mental health…