Objective There are many barriers to accessing mental health assessments including cost
and stigma. Even when individuals receive professional care, assessments are intermittent …

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

There are rising rates of depression on college campuses. Mental health services on our
campuses are working at full stretch. In response researchers have proposed using mobile …

Early detection of mental health changes in individuals with serious mental illness is critical
for effective intervention. CrossCheck is the first step towards the passive monitoring of …

Background: Technological advancements, together with the decrease in both price and
size of a large variety of sensors, has expanded the role and capabilities of regular mobile …

Collecting and analyzing data from sensors embedded in the context of daily life has been
widely employed for the monitoring of mental health. Variations in parameters such as …

The emergence of artificial emotional intelligence technology is revolutionizing the fields of
computers and robotics, allowing for a new level of communication and understanding of …

Background Electronic mental health interventions for mood disorders have increased
rapidly over the past decade, most recently in the form of various systems and apps that are …

Background Digital phenoty** is the use of data from smartphones and wearables
collected in situ for capturing a digital expression of human behaviors. Digital phenoty** …

Depression is the major cause of years lived in disability world-wide; however, its diagnosis
and tracking methods still rely mainly on assessing self-reported depressive symptoms …