A New Method Design for Multi-modal Depression Auxiliary Diagnosis from Perspective of Psychology

Abstract

Current depression diagnostic methods are subjective and single in nature. Therefore,
an objective screening mechanism based on physiological and behavioral signals is needed.
Through feature extraction and fusion of multi-modal data, we propose a design idea of
combining the deep learning algorithm with classical psychological experiment technology,
which enriches the idea of depression diagnosis and provides an objective basis for clinical
diagnosis.