A Framework of Intelligent Mental Health Monitoring in Smart Cities and Societies

Abstract

In any smart city and society, the citizens' mental health is one of the utmost concerns. Nowadays, people from different sectors of our community face a severe mental health threat due to the prolonged pandemic of COVID-19. Depression, anxiety, suicidal behaviours, and posttraumatic stress disorder are widespread terms nowadays for students, health care workers, jobless people, etc. And Machine Learning (ML), image processing, expert systems, Internet of Things (IoT) are performing an essential function in the significant acceleration of the automation process within the healthcare industry. Therefore, this article aims to address the problem of preventing mental health disorders by early predicting individuals using the developed web portal Mind Turner; and by integrating the mentioned emerging tools in this way, later chronic mental health disorders can be avoided. We used the Random Forest Classifier to detect stress levels from the Question-Answer-based assessment, and SVM is used to detect facial emotions. Finally, both are combined using Interval Type-2 Fuzzy Logic to predict the probable mental health of a person, i.e. acute depression, moderate depression and not depressed.