Research

The Data Analytics in Sport Science research group is interested in applied machine learning and data science, with a focus on understanding human movement, lifestyle behaviors, and their relationships with health and disease. We develop and apply computational and statistical methods to data from wearable sensors, digital health technologies, and large-scale population studies to uncover meaningful patterns in human behavior and health.

Our research spans wearable accelerometry, 24-hour movement behaviors, activity recognition, digital biomarkers, behavioral profiling, and data-driven health. We also explore emerging approaches in artificial intelligence, including deep learning, large language models, and foundation models, to develop new ways of extracting knowledge from complex health and wearable data.

Our long-term goal is to empower people to take greater control of their lifestyle and health-related behaviors through objective, personalized, and actionable insights. By combining advances in sensing, data science, and AI with knowledge from sport and health sciences, we aim to translate complex data into better understanding, better decisions, and ultimately better health.

Research Topics

• Wearable-measured movement behavior and health
• Wearable accelerometry
• Activity recognition
• Compositional time-use data analysis
• Movement behavior profile analysis
• Data-driven health
• Deep learning
• Machine learning

Emerging Research Topics

• Large Language Models and their applications to health and wearable data
• Foundation models for wearables
• Quantified self
• Causal machine learning