Bupa’s purpose is longer, healthier, happier lives. Digital interventions are a key element in delivering on our promise, with their potential to have positive impact on millions of lives. Personalised approaches towards behaviour change can be more effective and deliver a better user experience. Central to a personalised program is delivering interventions at the right time for each user. As such, using time series data to analyse and predict human behaviour is a natural choice.
We discuss the opportunities and challenges in applying deep learning techniques to time based behaviour data, to improve effectiveness of digital health interventions.
Ekaterina Volkova-Volkmar is a researcher at Bupa, London, UK. She finished her PhD at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, in 2014. With research background in neuroscience, computer science, and computational linguistics, Ekaterina is interested in integrating deep learning methods into digital solutions for behaviour change. Her current focus is on developing intelligent digital coaching services to help people improve their lifestyles and prevent diseases. More broadly, her research aims to bring human-computer interaction to a new level of naturalness and utility by using adaptable and context-aware approaches to the analysis of human behaviour.