Visual Analytics for Pervasive Sensor Data


Advances in sensor technology have enabled the deployment of pervasive healthcare systems in increasingly larger scales. There is a ‘quantified self’ movement, which involves using wearable sensors to quantify external activities of daily living (ADL) and internal metabolism. Many studies show that these systems can improve well-being of their users via presenting such information with self-reflection tools. However, there is a lack of visualization tools to aid analytical investigations over the data collected from these systems, which could improve our understanding on factors that influence well-being, and guide the design for better wellness management systems.

We develop a visual analytics tool to discover the interpersonal and intrapersonal patterns of ADLs. With this tool, we aim to observe possible influencing factors of well-being, which could be use to construct or validate models for statistical analysis.

This project is part of a larger framework of lifestyle recommenders research, and proceeds in parallel with “mining and analysing factors of well-being project.


Activities of daily living, visualization, visual analytics, qualitative user study




Onur Yuruten and Pearl Pu




Demo link