Mental illness is becoming a major plague in modern societies and poses challenges to the capacity of current public health systems worldwide. With the widespread adoption of social media and mobile devices, and rapid advances in...
moreMental illness is becoming a major plague in modern societies
and poses challenges to the capacity of current public
health systems worldwide. With the widespread adoption of
social media and mobile devices, and rapid advances in artificial
intelligence, a unique opportunity arises for tackling
mental health problems. In this study, we investigate how
users’ online social activities and physiological signals detected
through ubiquitous sensors can be utilized in realistic
scenarios for monitoring their mental health states. First, we
extract a suite of multimodal time-series signals using modern
computer vision and signal processing techniques, from
recruited participants while they are immersed in online social
media that elicit emotions and emotion transitions. Next,
we use machine learning techniques to build a model that
establishes the connection between mental states and the extracted multimodal signals. Finally, we validate the effectiveness of our approach using two groups of recruited subjects.
