Stay well: Analysing lifestyle data from smart monitoring devices.

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Pervasive health monitoring devices provide a rich data source with opportunity to continuously extract patterns and guide individuals towards their goals of wellbeing. To exploit this nexus between machine learning and pervasive computing, this project aims to solve the computational problems to analyse data from such wearable devices, applying rigorous statistical models to discover latent patterns and groupings. The significance lies in solving fundamental problems related to heterogeneous, multi-level, mixed-type time series data. The proposed outcomes are expected to enable monitoring of people 'in the wild', away from doctors and hospitals, thus significantly reducing the burgeoning cost of hospital visits and stays.