Depression is one of the most common mental disorders that affects more than 320 million people worldwide.  Depression is considered a primary cause of disability and suicide. Regrettably, less than 50% of individuals suffering from depression receive proper treatment. Moreover, depression causes one death every 40 seconds, adding up to 800,000 deaths by suicide worldwide every year.

The joint research between the University of Alberta and Tecnológico de Monterrey will develop data infrastructure and machine learning tools to detect depression, by analyzing conversational audio, biomarkers, and psychophysiological profiles. With all these data, a new and novel dataset will be created. This dataset will be the essentials for well designed predicting machine learning models. The joint team will develop the machine learning model(s) best suited to the dataset and determine with high accuracy, the depression level of a person. Additionally, it would be the first dataset of its kind that can be used by the research community for future machine learning studies on depression. Moreover, we envisage the development of a platform based on wearables and embedded artificial intelligence capable of alerting the subjects of psychophysiological symptoms related to depression in real-time. With this preliminary assessment, the individual can promptly be directed to the appropriate channel (i.e. a psychological service), where trained personnel assist in avoiding any negative impact that the depressive symptoms could have in his/her life and environment.