WeHeart: A Personalized Recommendation Device for Physical Activity Encouragement and Preventing "Cold Start" in Cardiac Rehabilitation

Rosa Van Tuijn, Tianqin Lu, Emma Driesse, Koen Franken, Pratik Gajane, Emilia I. Barakova

Abstract: This paper highlights the importance of physical activity in cardiac rehabilitation as a means of reducing morbidity and mortality rates associated with cardiovascular disease. However, forming physical activity habits is a challenge, and the approach varies depending on individual preferences. We introduce WeHeart, a personalized recommendation device that aims to gradually increase physical activity levels and avoid a “cold start”. WeHeart employs a Random Forest classification model that combines both measured and self-reported data to provide personalized recommendations. The system also uses Explainable AI to improve transparency and foster trust. Our study showcases the potential of Machine Learning in providing personalized recommendations for physical activity, and we propose a reinforcement learning approach to improve the system’s personalization over time. Overall, this study demonstrates the potential of WeHeart in encouraging physical activity and preventing “cold start” in cardiac rehabilitation.

External IDs:dblp:conf/interact/TuijnLDFGB23