By Malik Quirtas, Insight at University College Cork

Smartphones and wearable devices  are equipped with powerful sensors; such as accelerometers, GPS and gyroscopes; that can track our daily routines and behaviours passively. For example, accelerometers measure movement and physical activity while GPS can track locations and travel patterns. Collectively, these sensors provide data on how active we are, where we spend our time, how much we sleep and even social interactions – like how often we are near other people or in social gatherings –  all without requiring us to actively input any information.

My research focuses on using this passive data to detect signs of mental health issues like loneliness or depression. The idea is simple but powerful: as our devices collect data in the background, we also ask users how they are feeling through standard mental health surveys. By comparing their actual responses with the data collected from sensors, we train artificial intelligence models to recognise early signs of mental health conditions. The main goal is to spot these issues early, so we can step in before things get worse and help improve mental health. This approach could one day lead to mental health support that is personalised, timely and seamlessly integrated into everyday life.