Project R-15331

Mining Human Behavior Patterns from Sensor Data: Exploring the relation between (non-)sedentary behavior and chronic diseases. (Research)

This research project explores how behavioral analytics can be applied to data from smart devices, like activity trackers, to help us understand human (non-)sedentary behavior and its impact on chronic diseases such as type 2 diabetes and cardiovascular diseases. Our objectives are to: 1. Extract Behavioral Patterns: Convert raw sensor data into understandable patterns that capture (non-)sedentary behavioral patterns. 2. Visualize Patterns Effectively: Develop clear and insightful visual representations of these patterns to make them easier to interpret, focusing on the inherent time dimension of digital trace data. 3. Link Patterns to Health Effects: Use statistical methods to examine how these behavioral patterns are associated with health indicators related to chronic diseases. The ultimate goal is to enhance our understanding of how the application of behavioral analytics within the field of sports medicine can help to reveal how (non-)sedentary behavioral patterns influence chronic diseases.

01 January 2025 - 31 December 2026