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   Personal and ubiquitous sensing technologies such as smartphones have allowed the continuous collection of data in an unobtrusive manner.

   Machine learning methods have been applied to continuous sensor data to predict user contextual information such as location, mood, physical activity, etc.

   Recently, there has been a growing interest in leveraging ubiquitous sensing technologies for mental health care applications, thus, allowing the automatic continuous monitoring of different mental conditions such as depression, anxiety, stress, and so on. 

   Mental health monitoring systems (MHMS) using data and machine learning offer a classification taxonomy to best group social media users with other users experiencing the same issues.

   Conventional methods for monitoring the well-being of individuals with serious mental illness (SMI) are labor-intensive and rely on subjective assessments from clinicians.

   Sharing personal struggles and complaints on social media provides artificial intelligence with a rich dataset for analyzing and potentially predicting SMI based on our online behavior