Pediatric mental health is a growing concern around the world, with mental, emotional, and behavioral disorders affecting children's social-emotional development and increasing the risk of adverse behavioral outcomes later in life. However, diagnosing mental health disorders in early childhood remains challenging. Caregivers are often unable to accurately identify signs of problematic behavior and many lack access to specialized screening services.
Digital biomarkers from passively sensed signals collected using smartphones and wearable devices have shown remarkable promise for mental health screening at scale. Nevertheless, such digital mental health tools are yet to make a significant mark in pediatric settings. While this may be partly driven by caregivers' perspectives toward such tools, the fact that children rarely tend to be independent users of mobile and wearable devices is also a key deterrent to developing scalable digital biomarkers of mental health in younger populations.
In this thesis, I make the case for an alternative sensing paradigm leveraging multimodal signals recorded during children's existing play-based interactions. I demonstrate the technical feasibility of developing machine learning models to detect interaction-based biomarkers using behavioral (audio, video), physiological (heart rate, electrodermal activity), and neural (prefrontal cortex activation) signals, which are indicative of children's risk of attention-deficit/hyperactivity, disruptive behavior, and other externalizing disorders. Additionally, I investigate caregiver perspectives toward automated mental health screening tools, drawing upon my findings to develop richer, more usable biomarkers. This work thus sets the stage for future mobile and wearable technologies that can enable screening for early childhood mental health concerns at scale.
Advisors: Tauhidur Rahman and Deepak Ganesan