USC Researchers Combine Multimodal Signals with AI to Detect Depression and Suicide Risk
Researchers at the University of Southern California's Viterbi School of Engineering have published findings on a novel approach to detecting depression and suicide risk using a combination of physiological measurements and artificial intelligence.
The study integrates three distinct types of biosignals: electrodermal activity (sweat), brain signals (likely via