Stay well: Analysing lifestyle data from smart monitoring devices.

无处不在的健康监测设备提供了丰富的数据源，有机会不断提取模式并引导个人朝着他们的幸福目标前进。为了利用机器学习和普适计算之间的这种联系，该项目旨在解决计算问题，以分析来自这类可穿戴设备的数据，应用严格的统计模型来发现潜在模式和分组。其意义在于解决了与异质、多层次、混合型时间序列数据相关的基本问题。预计拟议的结果将使人们能够在野外监测远离医生和医院的人，从而显著降低迅速增长的医院就诊和停留成本。

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.