Abstract

Research on mobile sensing for mental health monitoring has traditionally explored the correlation between smartphone and wearable data with self-reported mental health symptom severity assessments. The effectiveness of predictive techniques to monitor depression is limited, given the idiosyncratic nature of depression symptoms and the limited availability of objectively labelled depression sensor-driven behaviour. In this paper, we investigate the possibility of using unsupervised anomaly detection methods to monitor the fluctuations of mental health and its severity. Informed by literature, we created a mobile application that collects acknowledged data streams that can be indicative of depression. We recruited 11 participants for a 1-month field study. More specifically, we monitored participants’ mobility, overall smartphone interactions, and surrounding ambient noise. The participants provided three self-reports: Big five personality traits, sleep and depression. Our results suggest that digital markers, combined with anomaly detection methods are useful to flag changes in human behaviour over time; thus, enabling mobile just-in-time interventions for in-the-wild assistance.