A framework for depression dataset to build automatic diagnoses in clinically depressed Saudi patients

Depression is a public health problem that has high effects on a person's functional and social relationships. Depression is a growing problem in the society. It causes pain and suffering not only to patients, but also to those who care about them. Depression disorder is hard to diagnose, because its symptoms could be confused with other disorders and has different cross-cultural symptoms. This paper proposes a framework that would best solve the problem of automatic depression detection in depressed Saudi patients. This paper particularly focuses on designing the collection of Saudi depression dataset using multiple modalities. The proposed framework uses pattern recognition to detect depression by analyzing facial expressions, eye movements, head movements, body movements, heart rate, and body temperature as well as their combination in multimodal method.