Future of mental health sensing: call to arms
Interaction Design Lab
Level 9, Doug McDonell Building (Building 168)
Mental illness touches almost every family these days. It negatively affects mood, behavior and functioning, and sadly still remains stigmatised even in these more enlightened times. Symptoms vary depending on the type of illness. Take depression on college campuses: first year students entering college can quickly feel stressed, anxious, isolated and overwhelmed, making them more susceptible to depressive episodes, dropping out of college, even suicide. People living with serious mental illness such as schizophrenia can suffer severe hardship including homelessness, victimisation and incarceration. Current approaches have failed.
Over the last decade, we have seen the rise of mobile sensing capable of assessing, tracking and predicting trajectories of mental health using mobile phones and wearables. However, most reported results are limited and come from small scale studies. If this nascent “mental health signal” proves to generalise across different populations and types of illness (eg, anxiety, bipolar) it will have profound and long lasting impact on the health and wellbeing of over 450 million people living with mental health illness on a daily basis worldwide.
In my talk, I will first discuss results from some recent projects on depression and schizophrenia sensing. I will make the case that future mental sensing technology will be mobile first and driven by AI. It will combine personalised sensing and intervention at population scale, and lead to new breakthroughs potentially directly connecting human behaviors passively sensed and inferred from mobiles to brain functioning. I’ll argue that a decade or so from now mental health sensing will be cheap, robust, mobile, ubiquitous and always-on. Ultimately, it will replace traditional episodic clinic-based assessments, which has left millions of people behind because of the lack of access to services. To democratise mental health and make mental health sensing globally accessible to everyone with a phone much more research is needed. Importantly, a business case still needs to be made. My talk is a call to arms: we roughly know how to do this technologically, so let’s get it done, why wait?
Andrew Campbell, Professor
Andrew T. Campbell is the Albert Bradley 1915 Third Century Professor in Computer Science at Dartmouth College. He is known for his pioneering work in mobile phone sensing. Some of the activity inference methods developed by his group are now common in all smartphones. Before joining Dartmouth, he was a tenured Professor of Electrical Engineering at Columbia University. He has been a visiting professor at CMU Rwanda, University of Salamanca and Cambridge University. Recently, he joined Google and worked on cardiovascular health as a member of the Android group and later as a visiting research scientist at Verily Life Sciences working on mental health sensing. His work has received a number of awards (e.g., ACM SIGMOBILE Test of Time Paper Award 2019 where his group "pioneered applying machine learning across local devices and servers") and has been covered widely by the popular press (New York Times, Financial Times, Economist), TV (BBC, CBS) and radio (NPR).