To develop an AI algorithm for continuous monitoring of mental health status using publicly available datasets

Poor mental health and stress are an expected outcome of the COVID-19 pandemic. Social distancing is taking another toll on the mental health of individuals. With most of the medical consultations being held online there is an urgent need to enable continuous monitoring of mental health by identifying risk factors for high stress and poor mental health and to provide individuals with information to improve their health and well-being. Wearable and mobile devices are an efficient and effective mean to achieve this goal in a very cost-effective manner. We would like to develop a new AI algorithm that will help assess mental health status of individuals in a real time fashion by using the continuous data feed from wearable devices. The aim of this project is to examine how accurately these measures could identify conditions of stress and poor mental health. We plan to apply novel algorithms on the already available datasets that are available in public domain to identify correlation between the various physiological markers and the poor mental health.

Faculty Supervisor:

Steven Wang

Student:

Hang Du

Partner:

C2C Healthcare Inc

Discipline:

Statistics / Actuarial sciences

Sector:

Professional, scientific and technical services

University:

York University